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Altini, Nicola, Giacomo Donato Cascarano, Antonio Brunetti, et al. "A Deep Learning Instance Segmentation Approach for Global Glomerulosclerosis Assessment in Donor Kidney Biopsies." Electronics 9, no. 11 (2020): 1768. http://dx.doi.org/10.3390/electronics9111768.
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The histological assessment of glomeruli is fundamental for determining if a kidney is suitable for transplantation. The Karpinski score is essential to evaluate the need for a single or dual kidney transplant and includes the ratio between the number of sclerotic glomeruli and the overall number of glomeruli in a kidney section. The manual evaluation of kidney biopsies performed by pathologists is time-consuming and error-prone, so an automatic framework to delineate all the glomeruli present in a kidney section can be very useful. Our experiments have been conducted on a dataset provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital. This dataset is composed of 26 kidney biopsies coming from 19 donors. The rise of Convolutional Neural Networks (CNNs) has led to a realm of methods which are widely applied in Medical Imaging. Deep learning techniques are also very promising for the segmentation of glomeruli, with a variety of existing approaches. Many methods only focus on semantic segmentation—which consists in segmentation of individual pixels—or ignore the problem of discriminating between non-sclerotic and sclerotic glomeruli, so these approaches are not optimal or inadequate for transplantation assessment. In this work, we employed an end-to-end fully automatic approach based on Mask R-CNN for instance segmentation and classification of glomeruli. We also compared the results obtained with a baseline based on Faster R-CNN, which only allows detection at bounding boxes level. With respect to the existing literature, we improved the Mask R-CNN approach in sliding window contexts, by employing a variant of the Non-Maximum Suppression (NMS) algorithm, which we called Non-Maximum-Area Suppression (NMAS). The obtained results are very promising, leading to improvements over existing literature. The baseline Faster R-CNN-based approach obtained an F-Measure of 0.904 and 0.667 for non-sclerotic and sclerotic glomeruli, respectively. The Mask R-CNN approach has a significant improvement over the baseline, obtaining an F-Measure of 0.925 and 0.777 for non-sclerotic and sclerotic glomeruli, respectively. The proposed method is very promising for the instance segmentation and classification of glomeruli, and allows to make a robust evaluation of global glomerulosclerosis. We also compared Karpinski score obtained with our algorithm to that obtained with pathologists’ annotations to show the soundness of the proposed workflow from a clinical point of view.
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Han, Yutong, Zhan Zhang, Yafeng Li, et al. "FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images." Cells 12, no. 23 (2023): 2753. http://dx.doi.org/10.3390/cells12232753.
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Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.
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Altini, Nicola, Giacomo Donato Cascarano, Antonio Brunetti, et al. "Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections." Electronics 9, no. 3 (2020): 503. http://dx.doi.org/10.3390/electronics9030503.
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The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.
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Dimitri, Giovanna Maria, Paolo Andreini, Simone Bonechi, et al. "Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images." Mathematics 10, no. 11 (2022): 1934. http://dx.doi.org/10.3390/math10111934.
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Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.
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Javvadi, Sai. "Evaluating the Impact of Color Normalization on Kidney Image Segmentation." International Journal on Cybernetics & Informatics 12, no. 5 (2023): 93–105. http://dx.doi.org/10.5121/ijci.2023.120509.
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The role of deep learning in the recognition of morphological structures in histopathological data has progressed significantly. But, less intensive preprocessing stages and their contribution to deep learning pipelines is often overlooked. Color normalization (CN) algorithms are among the most prominent methods in this stage, and they work by standardizing the staining pattern of a dataset. However, the impact of various color normalization algorithms on the detection of glomeruli functional tissue units (FTUs) in kidney tissue data has not been explored before. An advanced deep learning architecture was built with the U-NET segmentation model. The U-NET model is an architecture that specializes in the segmentation of biomedical data. A dataset of 15 kidney whole slide images (WSIs), each annotated with locations of glomeruli FTUs were processed and subsequently normalized according to three 3 different conventional color normalization techniques (Reinhard, Vahadane, Macenko), and fed into a U-NET model. The dice score coefficient (DSC) was used to compare the results of each run. It was determined that color normalization algorithms significantly impact the segmentation results of deep learning algorithms, with the Reinhard algorithm being the best technique. The implications of this work are immense, as it could contribute to the proliferation of color normalization techniques in preprocessing deep learning workflows, which could improve general segmentation accuracies.
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Hermsen, Meyke, Thomas de Bel, Marjolijn den Boer, et al. "Deep Learning–Based Histopathologic Assessment of Kidney Tissue." Journal of the American Society of Nephrology 30, no. 10 (2019): 1968–79. http://dx.doi.org/10.1681/asn.2019020144.
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BackgroundThe development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid–Schiff (PAS).MethodsWe trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network’s glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies.ResultsThe weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was “glomeruli” in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by “tubuli combined” and “interstitium.” The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures.ConclusionsThis study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.
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Kawazoe, Yoshimasa, Kiminori Shimamoto, Ryohei Yamaguchi, et al. "Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy." Diagnostics 12, no. 12 (2022): 2955. http://dx.doi.org/10.3390/diagnostics12122955.
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The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.
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Marechal, Elise, Adrien Jaugey, Georges Tarris, et al. "Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples." Clinical Journal of the American Society of Nephrology 17, no. 2 (2021): 260–70. http://dx.doi.org/10.2215/cjn.07830621.
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Background and objectivesThe prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features.Design, setting, participants, & measurementsIn total, 241 samples of healthy kidney tissue were split into three independent cohorts. The “Training” cohort (n=65) was used to train two convolutional neural networks: one to detect the cortex and a second to segment the kidney structures. The “Test” cohort (n=50) assessed their performance by comparing manually outlined regions of interest to predicted ones. The “Application” cohort (n=126) compared prognostic histologic data obtained manually or through the algorithm on the basis of the combination of the two convolutional neural networks.ResultsIn the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (>90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness, respectively. The algorithm had a good ability to predict significant (>25%) tubular atrophy and interstitial fibrosis level (receiver operator characteristic curve with an area under the curve, 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (>50%) (area under the curve, 0.85).ConclusionThis freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histologic data in a fast, objective, reliable, and reproducible way.
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Dr. Harikiran Jonnadula, Sitanaboina S. L. Parvathi,. "Small Blob Detection and Classification in 3D MRI Human Kidney Images Using IMBKM and EDCNN Classifier." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 629–42. http://dx.doi.org/10.17762/turcomat.v12i5.1061.
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The spatial and temporal resolution is dramatically increased due to the quick development of medical imaging technology, which in turn increases the size of clinical imaging data. Typically, it is very challenging to do small blob segmentation as of Medical Images (MI) but it encompasses so many vital applications. Some examples are labelling cell, lesion, along with glomeruli aimed at disease diagnosis. Though various detectors were suggested by the prevailing method for this type of issue, they mostly used 2D detectors, which may render less detection accuracy. To trounce this, the system has developed an efficient small Blob Detection (BD)as well as classification in 3D Magnetics Resonance Imaging (MRI) human kidney images utilizingImproved Mini Batch K-Means (IMBKM)and Enhanced Deep Convolutionals Neural Network (EDCNN) classifier. To segment the blob portions,the image is first ameliorated via Enhanced Contrast Limited Adaptive Histogram Equalization (ECLAHE) followed by the IMBKM algorithm. After that, to determine the segmentation performance, the pixels’ percentage in the detected blob portion is gauged. In addition, statistical, GLCM, together with shape features are extracted as of the segmented blob potions. Lastly, the EDCNN takes care of the classification, which classifies '4' classes, say, Normal, Glomerulonephritis, Stone, and Pyelonephritis. The experimental outcomes exhibit that IMBKM and EDCNN have the potential to automatically detect blobs and classify the blobs efficiently than the top-notch methods.
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Celia, A. I., X. Yang, M. A. Petri, A. Rosenberg, and A. Fava. "POS0288 DEGRANULATING PR3+ MYELOID CELLS CHARACTERIZE PROLIFERATIVE LUPUS NEPHRITIS." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 385.2–386. http://dx.doi.org/10.1136/annrheumdis-2023-eular.767.
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BackgroundDespite optimal treatment, lupus nephritis (LN) remains associated with irreversible kidney damage[1]. A better understanding of the mechanisms underlying LN pathogenesis is needed to develop better treatment targets. As part of the Accelerating Medicines Partnership (AMP), we discovered that urinary PR3, a myeloid degranulation product, correlated with histological activity implicating neutrophil/monocyte degranulation in proliferative LN, the most aggressive type[2]. PR3 is a serine protease that can mediate kidney damage. Mature neutrophils with classical polylobate nuclei are rare in LN kidney biopsies. However, recent evidence displayed how immature, degranulating myeloid cells have been implicated in the pathogenesis of LN[3], but their role in mediating kidney damage is not completely understood.ObjectivesTo investigate PR3+ cells in LN kidney, their association with histopathological features, and define their immunophenotype.MethodsWe performed multiplexed histology using serial immunohistochemistry (sIHC)[4]on archival LN kidney biopsies to quantify the expression of PR3 and multiple cell lineage markers (20-plex). Image analysis including deconvolution, cell segmentation, glomerular annotation, and quantitative histology was performed using Indica HALO. The analysis was limited to renal cortex.ResultsA total of 11 patients with LN who underwent a clinically indicated kidney biopsy were enrolled: 6 (55%) with pure proliferative LN (ISN/RPS class III or IV) and 5 (45%) with pure membranous LN. PR3+ cells were identified in all LN biopsies (range 343-7625 per sample). Most PR3+ cells did not show a polylobate nucleus. The majority of PR3+ cells were in the tubulointersitium (Figure 1A). However, accounting for the smaller glomerular area, there was a higher density of PR3+ cells in the glomeruli (Figure 1A-C). PR3+ cell abundance was higher in proliferative LN, especially in the glomeruli (Figure 1A-C). Glomerular PR3+ cell density very strongly correlated with histological activity measured by the NIH Activity Index (Pearson’s r=0.97, p=5*10-5; Figure 1D). Preliminary serial IHC analysis showed that PR3+ cells coexpressed MPO and variably coexpressed CD66b and CD14, but not neutrophil elastase, CD3, or CD20.Figure 1.ConclusionPR3+ cells are abundant in LN. PR3+ cells are increased in proliferative LN and are strongly associated with histological activity thereby characterizing a more aggressive phenotype. This population densely infiltrated the glomeruli emphasizing a potential role in the endothelial pathogenic process. In preliminary analysis, kidney infiltrating PR3+ cells were not polymorphonucleated, did not express neutrophil elastase, and variably expressed CD14 suggesting a phenotype consistent with degranulating monocyte or an immature myeloid population. We previously showed the association between urinary PR3 and histological activity suggesting that intrarenal PR3+ cells are actively degranulating and therefore likely inducing kidney damage. These findings nominate PR3+ cells as a potential therapeutic target. Spatial transcriptomics and proteomic studies are ongoing to define the lineage and function of these cells.References[1]Mahajan, A., et al. Systemic lupus erythematosus, lupus nephritis and end-stage renal disease: a pragmatic review mapping disease severity and progression. Lupus 29, 1011-1020 (2020)[2]Fava, A., et al. A Neutrophil Degranulation Signature Identifies Proliferative Lupus Nephritis. Arthritis Rheumatol. 2021; 73 (suppl 9).[3]Mistry, P. et al. Transcriptomic, epigenetic, and functional analyses implicate neutrophil diversity in the pathogenesis of systemic lupus erythematosus. Proc. Natl. Acad. Sci. U. S. A. 116, 25222–25228 (2019)[4]Akturk, G., Sweeney, R., Remark, R., Merad, M. & Gnjatic, S. Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS): Multiplexed Chromogenic IHC Assay for High-Dimensional Tissue Analysis. Methods Mol. Biol.2055, 497–519 (2020).Acknowledgements:NIL.Disclosure of InterestsNone declared.
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Hara, Satoshi, Emi Haneda, Masaki Kawakami, et al. "Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules." PLOS ONE 17, no. 7 (2022): e0271161. http://dx.doi.org/10.1371/journal.pone.0271161.
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Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.
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Zlobina, Olga V., Alexey N. Ivanov, Taisiya V. Milashevskaya, Valeria Yu Seryogina, and Irina O. Bugaeva. "Comparative analysis of morphological changes in renal tissue under the influence of light desynchronosis." Morphology 159, no. 2 (2022): 63–70. http://dx.doi.org/10.17816/1026-3543-2021-159-2-63-70.
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AIM: To compare morphological changes that occur in renal tissue, as a result of exposure to various models of light desynchronosis.
 MATERIAL AND METHODS: This study was conducted on 48 white rats. Three experimental groups were exposed to light for 21 days. The LL (0:24) model was studied in the first group, while the LD 18:6 and 12:10 models were studied in the second and third groups, respectively. The control group was kept in natural conditions all through the experiment.
 The animals were placed under anesthesia with a combination of Telazol (ZoetisInc, USA) and Xylanit (Nita-farm, Russia). Afterward, their right kidney was removed. The samples obtained were prepared according to the standard method. Statistical processing was performed using the package of applied statistical programs "STATISTICA 10" (StatSoft , USA).
 RESULTS: Morphological disorders of the renal tissue were observed in the three experimental groups. In the first experimental group, there was a significant segmentation of the glomeruli, accompanied by dystrophic changes in the renal tubules. In the second experimental group, glomerular segmentation was more pronounced. In the renal tissue of animals of the third experimental group, the disorders were highly observable, and the sclerotized segment is noted. Changes in morphometric indicators were significant across all experimental groups.
 CONCLUSION: Desynchronosis harms the renal tissue by causing changes in its morphology. The most significant disorders characterized by sclerosis were observed in the kidneys of animals in the third experimental group.
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Hao, Fang, Xueyu Liu, Ming Li, and Weixia Han. "Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy." Life 13, no. 2 (2023): 399. http://dx.doi.org/10.3390/life13020399.
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Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron microscopy, and immunofluorescence microscopy. Manual observation of glomeruli one by one under the microscope is very time-consuming, and there are certain differences in the observation results between physicians. This study makes use of whole-slide images scanned by a light microscope as well as immunofluorescence images to classify patients with membranous nephropathy. The framework mainly includes a glomerular segmentation module, a confidence coefficient extraction module, and a multi-modal fusion module. This framework first identifies and segments the glomerulus from whole-slide images and immunofluorescence images, and then a glomerular classifier is trained to extract the features of each glomerulus. The results are then combined to produce the final diagnosis. The results of the experiments show that the F1-score of image classification results obtained by combining two kinds of features, which can reach 97.32%, is higher than those obtained by using only light-microscopy-observed images or immunofluorescent images, which reach 92.76% and 93.20%, respectively. Experiments demonstrate that considering both WSIs and immunofluorescence images is effective in improving the diagnosis of membranous nephropathy.
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Shen, Luping, Wenyi Sun, Qixiang Zhang, et al. "Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis." Kidney Diseases 8, no. 4 (2022): 326–35. http://dx.doi.org/10.1159/000524880.
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<b><i>Background:</i></b> Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis. <b><i>Methods:</i></b> We developed a murine renal pathological system (MRPS) model to objectify the pathological evaluation via the deep learning method on whole-slide image (WSI) segmentation and feature extraction. A convolutional neural network model was used for accurate segmentation of glomeruli and glomerular cells of periodic acid-Schiff-stained kidney tissue from healthy and lupus nephritis mice. To achieve a quantitative evaluation, we subsequently filtered five independent predictors as image biomarkers from all features and developed a formula for the scoring model. <b><i>Results:</i></b> Perimeter, shape factor, minimum internal diameter, minimum caliper diameter, and number of objects were identified as independent predictors and were included in the establishment of the MRPS. The MRPS showed a positive correlation with renal score (<i>r</i> = 0.480, <i>p</i> &#x3c; 0.001) and obtained great diagnostic performance in discriminating different score bands (Obuchowski index, 0.842 [95% confidence interval: 0.759, 0.925]), with an area under the curve of 0.78–0.98, sensitivity of 58–93%, specificity of 72–100%, and accuracy of 74–94%. <b><i>Conclusion:</i></b> Our MRPS for quantitative assessment of renal WSIs from MRL/lpr lupus nephritis mice enables accurate histopathological analyses with high reproducibility, which may serve as a useful tool for glomerulonephritis diagnosis and prognosis evaluation.
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Hölscher, David, Nassim Bouteldja, Yu-Chia Lan, Saskia von Stillfried, Roman David Bülow, and Peter Boor. "MO062: Pan-Disease Segmentation and Feature Extraction for Large-Scale Digital Nephropathology." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac063.014.
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Abstract BACKGROUND AND AIMS Nephropathology is essential for the diagnosis of kidney diseases. Deep learning-based image analyses, including segmentation of kidney histology, open new possibilities for reproducible quantitative precision pathology. Current segmentation approaches in kidney histology focused on specific use cases. Here, we developed a framework for automated segmentation and quantification of a wide spectrum of non-neoplastic kidney diseases. METHOD We trained U-Net Convolutional Neural Networks (CNNs) for two streamlined tasks: (i) detection of kidney tissue and (ii) instance segmentation of relevant histological structures, i.e. glomeruli, tubules and arteries. These were used to segment 1103 Whole-Slide-Images (WSIs) from four cohorts, including two external datasets for multi-center validation. In total, 35 features from 51 445 glomeruli, 4016 792 tubules and 362 471 arteries were extracted, generating 30 million morphometric data points. Morphometry data were associated with clinical and pathology data. RESULTS Kidney tissue was precisely outlined by the tissue segmentation CNN. The structure segmentation CNN for histology provided accurate results on WSI-level despite difficult histopathological morphologies such as crescents, segmental sclerosis, tubular casts or arteriosclerosis. Quantitative analyses of morphological alterations of glomeruli revealed, for example, an increased glomerular area in membranous glomerulonephritis and all cases characterized with nephrotic range proteinuria independent of the underlying disease, and decrease in glomerular tuft circularity in pauci-immune glomerulonephritis, especially in cases with severe loss of kidney function. Principal component analysis of multiple glomerular, tubular and interstitial features stratified based on estimated glomerular filtration rate identified CKD-relevant morphological alterations of glomeruli and the tubulointerstitium. Image extraction of morphometric outliers enabled fast-track visual assessment of severe lesions. CONCLUSION Segmentation and large-scale quantitative feature extraction enable reproducible quantitative analysis of kidney morphology, opening new possibilities for digital precision nephropathology.
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Klaus, Martin, Rosch Ronny Abdullah, Qiubo LI, Christoph Walz, Hans Joachim Anders, and Stefanie Steiger. "#439 Nephron number determination in whole slide mice kidney specimens using a deep learning assisted disector/fractionator approach." Nephrology Dialysis Transplantation 39, Supplement_1 (2024). http://dx.doi.org/10.1093/ndt/gfae069.277.
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Abstract Background and Aims Nephron number highly varies between different species and within species, ranging in humans from 200 000 to 2 000 000 nephrons per kidney. Low nephron numbers can promote early onset of end-stage kidney disease. Nephron assessment is highly needed for better diagnosis and more targeted treatment. However, its assessment in vivo is not established in clinical practice yet. Pioneer studies suggest nephron number estimation in vivo through a combination of glomerular densities in kidney biopsies together with medical imaging. In contrast, post-mortem nephron assessment either using optical clearing or by exhaustive kidney sectioning is well studied but remains laborious. Here, we propose a new semi-automated nephron counting approach using serial PAS-stained whole slide kidney sections, deep learning segmentation and slide registration. Method Mice were sacrificed, kidneys harvested, measured, halved in the coronary plane and then opposingly embedded in paraffin. Subsequently, the entire kidney was sectioned with 10 µm thickness per slide. Tissue was stained with PAS and slides were digitalized using Aperio GT 450 DX scanner (Leica, Wetzlar, Germany) (Fig. a). Deep learning segmentation of glomeruli was performed in the first and every 10th (reference) section and subsequent (look-up) section using Tensorflow. Obtained segmentation results were manually quality controlled and adjusted, if necessary, in QuPath (Fig. b). Final segmentations of the look-up slides were registered to the reference slides in ImageJ using manual landmarks with the ImageJ BigWarp plugin (Fig. c). Glomeruli being present either in the look-up slide, or in the reference slide were counted as disector particles (Q-). Kidney areas on every examined slide were measured. Total number of glomeruli and total kidney area were determined as previously described (Fig. d). Results A kidney from a 12-week-old mouse suffering chronic kidney disease (mouse line Alb-creERT2 (ki/ki); Glut9 (fl/fl)) was used. Kidney weight was 162 mg, length 11 mm, width 6 mm, depth 4 mm and kidney volume 260 mm³. In total the kidney was sectioned in 95 slices. 20 reference sections and its respective look-up sections were stained and digitalized. In total 5981 glomeruli were segmented in 40 kidney slices using the deep learning algorithm. Glomerular segmentation quality was verified by two independent observers (mean dice score 0.79). Inaccuracies, if any, where manually corrected. For image registration in average 18.1 landmarks per slide were created within the ImageJ BigWarp plugin. Registration took in average 5 minutes per slide. Glomeruli without match (disector particles, Q-) were automatically counted. Number of disector particles (Q-) ranged from 32 to 244 per slide. Total nephron number was 11.085 and kidney area 210 mm³. The differences to the manual measured volume (260 mm³) might be explained by discard of small kidney fractions for the used sectioning technique. Conclusion We report, to our knowledge, the first deep learning assisted disector/fractionator approach to determine nephron number in whole slide kidney specimens. This method requires no physical disector setup, is scalable, cheap, time efficient, and unbiased. The method was so far validated in a mouse model of chronic kidney disease. The method will further be validated on different animal models. Additionally, occurrence of atubular glomeruli will be considered in nephron number determination. Furthermore, the integration of GFR measurements might allow the calculation of mean single nephron GFR and might together with automated glomerular area measurements unravel pathophysiologic processes in kidney ageing and disease.
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Li, Xiang, Richard C. Davis, Yuemei Xu, et al. "Deep learning segmentation of glomeruli on kidney donor frozen sections." Journal of Medical Imaging 8, no. 06 (2021). http://dx.doi.org/10.1117/1.jmi.8.6.067501.
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Lutnick, Brendon, Katharina Moos, Surya V. Seshan, et al. "MO077AUTOMATIC SEGMENTATION OF ARTERIES, ARTERIOLES AND GLOMERULI IN NATIVE BIOPSIES WITH THROMBOTIC MICROANGIOPATHY AND OTHER VASCULAR DISEASES." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab078.0013.
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Abstract Background and Aims Thrombotic microangiopathies (TMAs) manifest themselves in arteries, arterioles and glomeruli. Nephropathologists need to differentiate TMAs from mimickers like hypertensive nephropathy and vasculitis which can be problematic due to interobserver disagreement and poorly defined diagnostic criteria over a wide spectrum of morphological changes with partial overlap. As a first step towards a machine learning analysis of TMAs, we developed a computer vision model for segmenting arteries, arterioles and glomeruli in TMA and mimickers. Method We manually segmented n=939 arteries, n=6,023 arterioles, n=4,507 glomeruli on whole slide images (WSIs) of 34 renal biopsies and their HE, PAS, trichrome and Jones sections (19 TMA, 11 hypertensive nephropathy, 4 vasculitis with preglomerular involvement). As a segmentation model we used DeepLab V3, pretrained on 61,734 segmented glomeruli from 768 WSIs. 58 randomly chosen WSIs served as the intrainstitutional holdout testing set after training of the model on the remaining slides. Automatic segmentation accuracies were reported as Cohen’s kappa, intersection over union (IoU) and Matthews correlation coefficient (MCC) against the nephropathologist’s segmentation as ground truth. Results Over all classes (artery, arteriole, glomerulus) Cohen’s kappa was 0.86. IoU was 0.716 for artery, 0.491 for arteriole and 0.829 for glomerulus. MCC was 0.837 for artery, 0.664 for arteriole and 0.907 for glomerulus. Conclusion We achieved good automatic segmentation of arteries, arterioles and glomeruli, even with severe pathological distortion on routine histopathological slides. We will further improve this segmentation technology in order to enable the bulk analysis of these descisive tissue compartments in large clinicopathological repositories of native kidney biopsies with TMA using supervised and unsupervised machine learning algorithms.
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MANSOUR, Mohammed, Mert Süleyman DEMİRSOY, and Mustafa Çağrı KUTLU. "Kidney Segmentations Using CNN models." Journal of Smart Systems Research, March 5, 2023. http://dx.doi.org/10.58769/joinssr.1175622.
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For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program (HuBMAP). The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to solve the issue. The high size images were decoded to be fitted and trained in the models first, then the CNN models were trained. The CNN models result show that the Efficient Net has the highest accuracy rate with 99.49 %, and Mobile Net with 99.33 %.
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Bhaskara, Suneil, Michael Ferkowicz, Daria Barwinska, and Tarek M. Ashkar (El-Achkar). "Three-dimensional morphometric analysis of human kidney nephron structures in health and disease." Proceedings of IMPRS 4, no. 1 (2021). http://dx.doi.org/10.18060/25866.
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Background and Hypothesis: Chronic kidney disease (CKD) is very common and affects as many as 37 million people in the United States. Diabetes and hypertension are the most common causes of CKD. The pathogenesis of CKD is not fully elucidated. Morphological changes such as glomerulosclerosis and tubular atrophy are commonly observed with advanced disease. However, it is unclear if such changes occur at earlier stages of disease, a time when therapeutic interventions are likely to have the most benefit. Measurements of glomerular, vascular and tubular dimensions have been typically derived from thin section, but a pipeline for acquiring these morphometric measurements in 3-dimentions have not been performed. Using enhanced tissue clearing, confocal 3D imaging and volumetric segmentation and analysis, we aimed at developing an approach to measure the dimensions of various structures within the human kidney. Our overarching hypothesis is that such approach could allow the detection of morphometric changes in early disease. 
 Experimental Design or Project Methods: Kidney tissue were used either from donor nephrectomies or stored frozen biopsies from Biobank Biopsy Cohort of Indiana (BBCI). Enhanced tissue clearing and staining for nuclei, endothelium and proximal tubules were performed. Optical sectioning and 3D Imaging was done using confocal microscopy. Volume rendering, segmentation and analysis were done using the Imaris Software (Bitplane). Various built in and customized segmentation approaches were used.
 Results: Volumes spanning 200 µm in thickness encompassing entire glomeruli and tubules were obtained. 3D Rendering of these volumes allowed visualization and enabled 3D segmentation. Dimensions of entire human glomeruli (maximum and minimum diameters, glomerular volumes and cellular densities), tubules and vessels were defined in control and samples with diabetic kidney disease.
 Conclusion and Potential Impact: Our studies established an approach for accurate measurements of the dimensions of nephronal structures preserved in their 3D environment within tissue. We also established the feasibility of this approach in comparing changes with disease vs. control. Such methodology will open up a new line of investigations to better understand the pathogenesis of CKD, particularly at its early stages.
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Marechal, Elise, Adrien Jaugey, Georges Tarris, et al. "FC046: Automated Mest-C Classification in IGA Nephropathy using Deep-Learning based Segmentation." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac105.002.
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Abstract BACKGROUND AND AIMS IgA nephropathy prognosis depends on histological factors. The MEST-C score is used to grade those factors and assess the renal prognosis of this disease. Nevertheless, this manual evaluation is time-consuming, tedious and has a poor reproducibility. An automated analysis would be faster and more objective. This work aimed to use deep-learning techniques on whole kidney biopsies from patients suffering from IgA nephropathy to obtain an automated MEST-C score. METHOD We used a previously developed convolutional neural network (CNN) to isolate the cortical area. Then, two additional CNNs were independently trained. The first one was used to evaluate the Interstitial Fibrosis/Tubular Atrophy (IF/TA) and segment the glomeruli. The second one was used to segment the relevant glomerular lesions (mesangial hypercellularity, endocapillary hypercellularity, segmental glomerulosclerosis and crescents). A total of 95 kidney biopsies from patients suffering from IgA nephropathy were randomly sorted into either the training set or the validation set. From those samples, regions of interest (ROI) were selected and annotated to develop the two CNNs (358 ROI and 458 ROI, respectively). The main annotation categories for the first CNN were `non-sclerotic glomeruli’, `sclerotic glomeruli’, `normal tubules’, `atrophic tubules’, `veins’ and `arteries’. For the second CNN, the main categories were `mesangial hypercellularity’, `endocapillary hypercellularity’, `segmental glomerulosclerosis’ and `crescents. Then, the three CNNs were sequentially applied to the biopsies of 109 patients suffering from IgA nephropathy. From the data provided by the CNNs, an automated MEST-C was calculated. We assessed its performances to predict the criteria of the gold standard MEST-C (scored by nephropathologists) using Receiver Operator Characteristic (ROC). RESULTS The CNNs detected the renal structures of interest with good precision and recall up to 0.98 and 0.96 respectively for the non-sclerotic glomeruli. Normal and atrophic were also reliably recognized with 0.88 and 0.71 f-scores. Glomerular lesions were harder to recognize with a 0.67 f-score for mesangial hypercellularity, 0.76 f-score for endocapillary hypercellularity, 0.47 f-score for segmental glomerulosclerosis and 0.50 f-score for the crescents. Manual and automated evaluation of IF/TA were well correlated with a 0.76 Spearman coefficient (P &lt; 0.001). In the application cohort, the criteria of the automated adapted MEST-C were compared with the criteria of the visual MEST-C using Receiver Operator Characteristic (ROC), we assessed the performances of the criteria of the automated MEST-C to predict the criteria of the manual MEST-C. Areas under the curve (AUC) were 0.82 for mesangial hypercellularity, 0.79 for endothelial hypercellularity and 0.82 for segmental glomerulosclerosis. Regarding IF/TA (the T criteria), the AUC were 0.86 for T1 and 0.84 for T2. Regarding the evaluation of crescents, the AUC were 0.76 for the C1 criteria and 0.88 for the C2 criteria. Those ROC were used to determine adapted thresholds for each criterion. CONCLUSION This deep-learning based tool can be used to segment relevant renal cortical structures and provide a reliable assessment of IF/TA. An automated MEST-C score can be provided by our tool. Nevertheless, improvements are needed to consider using this tool in clinical practice and obtain an objective evaluation on large and numerous samples. FIGURE 1:Kidney samples stained with Masson's trichrome before and after the two first consecutive convolutional neural networks predictions (B, C). B: Cortical area (red), capsule (deep blue), C: normal and atrophic tubules (red and orange), non-sclerotic glomeruli (yellow), sclerotic glomeruli (light blue), arteries (purple), vein (deep blue).FIGURE 2:Kidney samples stained with Masson's trichrome before (A) and after the last convolutional neural networks predictions (B). B: hilum (yellow), mesangial hypercellularity (red), endothelial hypercellularity (purple), segmental glomerulosclerosis (green).
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Jin, Feng Yong, Yang Huang, Yu Lin Huang, Qing Zhao, Qin Yi Wu, and Kun Ling Ma. "#2775 USING A DEEP LEARNING MODEL TO EVALUATE PATHOLOGICAL INJURY OF DIABETIC KIDNEY DISEASE." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063b_2775.
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Abstract Background and Aims Early diagnosis and evaluation play an important role in preventing the progression of diabetic kidney disease (DKD). Renal biopsy is the gold standard of DKD diagnosis. In 2010, the Renal Pathology Society (RPS) developed a consensus classification for DKD, which classifies DKD glomerular lesions and scores for tubulointerstitial lesion. However, as the pathologic heterogeneity of DKD patients and unparallel relationship between pathology features and clinical symptoms, it remains controversial whether is reliable to use RPS classification for renal outcomes prediction. Besides, the inconsistence between pathologists might magnify the gap between prediction and outcome. More reliable methods for DKD pathology assessment are needed. The development of machine learning (ML) algorithms especially convolutional neural networks (CNNs) enables it possible, which can provide automatic and precise analysis for complicated images. Thus, this study aimed to construct a ML-based model to assist and provide automatic decision supports to clinical doctors. Method We examined 204 DKD renal biopsied whole-slide images (WSIs) of Periodic Acid Schiff staining from Southeast University Institute of Nephrology whose database came from more than 30 hospital centers. All the WSIs were randomly annotated by three pathologists. We established a consecutive CNNs framework: DKD pathology evaluation system (DPES). It consists of three parts: 1. Based on classic UNet architecture, we developed a segmentation model whose backbone is replaced by ResNet 50 architecture for detecting glomeruli. 2. According to RPS classification, a CNN added with channel attention mechanism is built to discriminate sclerotic glomeruli, glomeruli with K-W node, and glomeruli with mesangial expansion. Through this method, the entire WSIs were divided into RPS grades I-IV. 3. For the analysis of tubulointerstitial lesion, we constructed a segmentation model as previous description to calculate the area of the atrophic tubule and its percentage of the total tubules. Results For glomerular assessment, compared with other ML networks, DPES achieved better performance in segmentation for glomeruli (Intersection over Union (IOU): 0.82, Precision: 0.89, Accuracy: 0.91) and tubulointerstitial (atrophy (TA): IOU: 0.9;Precision: 0.94; Accuracy: 0.95; non-tubular atrophy (NT): IOU: 0.84; Precision: 0.90; Accuracy: 0.93). Interstitial fibrosis and tubular atrophy (IFTA) from network compared with three pathologists didn’t show statistically difference. Conclusion Our findings demonstrated that the DPES achieved similar performance with three pathologists. It suggests that machine learning algorithms can generate reliable results and provide more supports for clinical decision.
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Feng, Chunyue, Kokhaur Ong, David M. Young, et al. "Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis." Bioinformatics, December 7, 2023. http://dx.doi.org/10.1093/bioinformatics/btad740.
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Abstract Motivation Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic Kidney Disease (CKD) is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an Artificial Intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). Results We collected 2,935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93,932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, p-value &lt; 0.001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. Availability https://github.com/ChunyueFeng/Kidney-DataSet. Supplementary information Supplementary data are available at Bioinformatics online.
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Lucarelli, Nicholas, Brandon Ginley, Jarcy Zee, et al. "Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine." Kidney360, November 14, 2023. http://dx.doi.org/10.34067/kid.0000000000000299.
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Background: Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and estimated glomerular filtration rate (eGFR) in a multinational set of reference kidney tissue sections. Methods: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. Results: Our deep-learning model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased, as a function of increasing age. Conclusions: Using deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis.
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Klaus, Martin, Manga Motrapu, Stefanie Steiger, and Hans Joachim Anders. "#2863 SEX-, AGING AND DIABETES-RELATED ALTERATIONS IN GLOMERULAR DIMENSIONS AND PODOCYTE DENSITIES USING DEEP-LEARNING QUANTIFICATION." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063c_2863.
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Abstract Background and Aims Kidney function, as well as its morphology, changes markedly with age and disorders such as diabetes. This process is associated with structural and functional alterations in cortical and juxtamedullary glomeruli. Currently, data on differences in cortical and juxtamedullary glomeruli associated with sex, age, genetic factors, and diabetes are limited. In this study, we investigated the abundance and morphometry of podocytes and glomeruli in mice of different ages and sex’, and suffering from diabetes or not using a deep-learning based analysis of immuno-stained kidney sections. Methods Male and female non-diabetic C57BL/6J mice and diabetes type II (db/db) mice were sacrificed at different time points: 4, 10, 20, 30, 34, 40 weeks and 6, 24 weeks, respectively. Subsequently, kidneys were extracted, embedded in paraffin, cut into sections, stained, and imaged for histological analysis. We used immunhistochemistry staining with Wilms Tumor 1 (WT1) antibody to specifically stain podocyte nuclei. Both manual and deep-learning based image segmentation were performed to analyze abundance and morphometry of podocytes and glomeruli. In total, 4134 glomerular structures were detected, and morphometry of glomeruli and podocytes was extracted and analyzed by an automated algorithm. Results Our study aimed to investigate aging- and diabetes-related differences in cortical and juxtamedullary glomeruli with respect to podocyte abundance and loss. Using a customized deep learning algorithm, podocyte nuclei could be quantified with comparable quality as a time-tedious manual analysis, which takes approximately 1 minute per glomerulus. Extracted morphometric features showed that juxtamedullary glomeruli had a larger cross-sectional area than cortical glomeruli (Figure 1a). For both cortical and juxtamedullary glomeruli the cross-sectional area slightly increased on average with aging (Figure 1b). As expected, podocyte endowment in cortical glomeruli was lower than in juxtamedullary glomeruli with podocyte density being higher (Figure 2a, 2b). Over life-time the number of podocytes and podocyte density per glomerulus slightly decreased both in cortical and juxtamedullary glomeruli (Figure 2b, 3b). Interestingly, female cortical glomeruli were on average smaller and had a higher podocyte density compared to males (Figure 1c, 3c). However, podocyte numbers did not differ between male and female (Figure 2c). Finally, we found that 24 weeks old db/db mice presented with glomerular hypertrophy in contrast to non-diabetic C57BL/6J mice of the same age (Figure 1d). Db/db mice lost podocytes from 6 weeks to 24 weeks of age with a decreased podocyte density. Surprisingly, podocyte loss occurred to a lower extent in non-diabetic mice (Figure 2d, 3d). Conclusion During aging and early diabetic disease both podocyte loss and glomerular hypertrophy occur. Similar changes occurred in juxtamedullary and cortical glomeruli in both sex’. Hyperfiltration might explain the pronounced extent in glomerular area in diabetic mice and should represent an increase in filtration surface to handle diabetes-related hyperfiltration. Less podocyte loss in diabetic mice at 24 weeks age compared to C57BL/6J mice might relate to the different mouse model and still moderate podocyte stress during the early stage of diabetic kidney disease.
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Luchian, Andreea, and Lorenzo Ressel. "Combined Colour Deconvolution and Artificial Intelligence approach for region‐selective immunohistochemical labelling quantification. The example of alpha Smooth Muscle Actin in mouse kidney." Journal of Biophotonics, October 25, 2023. http://dx.doi.org/10.1002/jbio.202300244.
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AbstractImmunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi‐quantitative and exhibits substantial intra‐and inter‐observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator‐dependent and time‐consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI definition. The method was tested on 10 Whole Slide Images (WSI) of Alpha‐Smooth Muscle Actin (aSMA) stained mouse kidney sections. The task was to identify aSMA‐positive areas within the glomeruli automatically. Total aSMA detection was performed using two channels (DAB, haematoxylin) colour deconvolution. Glomeruli segmentation within the same IHC WSI was performed by training a convolutional neural network with annotated examples of glomeruli. For both aSMA and glomeruli, binary masks were created. Co‐localisation was performed by overlaying the masks and assigning red/green colours, with yellow indicative of a co‐localised signal. The workflow described and exemplified using the case of aSMA expression in glomeruli can be applied to quantify the expression of IHC markers within different structures of immunohistochemically stained slides. The technique is objective, has a fully automated threshold approach (colour deconvolution phase) and uses AI to eliminate operator‐dependent steps.This article is protected by copyright. All rights reserved.
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Luchian, Andreea, Katherine Trivino Cepeda, Rachel Harwood, et al. "Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using Deep Learning-based semantic segmentation in histology." Biology Open, August 29, 2023. http://dx.doi.org/10.1242/bio.059988.
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This study focuses on Ischaemia-Reperfusion Injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a Convolutional Neural Network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular Casts and Tubular Necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal Tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman Correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.
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Mikhailov, Alexei V., Heng-Jie Cheng, Jen-Jar Lin, and Che Ping Cheng. "Abstract 9396: Calmodulin-Dependent Protein Kinase II Activation Promotes Kidney Mesangial Expansion in Streptozotocin-Induced Diabetic Mice." Circulation 146, Suppl_1 (2022). http://dx.doi.org/10.1161/circ.146.suppl_1.9396.
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Background: Calmodulin-dependent protein kinase II (CaMKII) is upregulated in diabetes mellitus (DM), leading to the overproduction of collagen in the myocardium. We hypothesized that CaMKII also stimulates the extracellular matrix production in the kidneys leading to mesangial matrix expansion, a hallmark of diabetic nephropathy and a major cause of renal failure. To test this hypothesis, we developed an automated process of mesangial matrix measurement. Methods: We induced Type 1 diabetes (DM1) in wild type female FVB mice via an intraperitoneal injection of Streptozotocin (STZ, 200 mg/kg ip) and studied the dynamics of mesangial matrix fractional area (MMFA) over 10 weeks (W) in mice with: DM , DM/KN93 , 6 W after STZ, followed by 4 W treatment with a CaMKII inhibitor KN93 (70 μg/kg/day, via mini pump), and Control (C) (vehicle only) mice. Both kidneys were retrieved. MMFA measurements were performed on 50 glomeruli from each animal on trichrome-stained paraffin sections using a trainable segmentation WEKA (Waikato Environment of Knowledge Analysis) plugin for Fiji, as well as on the electron micrographs of 4-5 whole glomeruli/animal. Results: Both DM and DM/KN93 mice were severely hyperglycemic (517 and 538 mg/dL compared to 161 mg/dL in C). In DM mice, the kidney weight increased by 76% and the MMFA significantly increased by 81% compared to C (9.4 ± 1% vs 5.2 ± 2.8%). In DM/KN93 mice, MMFA was similar to that in controls (6.2 ± 2.3%). The electron microscopy study has confirmed these MMFA changes. Other features of human diabetic nephropathy were not seen and the fractional mesangial cell area and the fractional podocyte area were relatively unchanged in DM and DM/KN93 mice. Conclusions: Our DM1 mouse model reproduces the key features of early human diabetic nephropathy. KN93 prevents DM-induced extracellular matrix production and kidney mesangial matrix expansion, suggesting that chronic inhibition of CaMKII could be a potential treatment for the early diabetic nephropathy. Reliable automated measurement of the MM area can be achieved after training by the WEKA plugin.
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Siegerist, Florian, Eleonora Hay, Julien Dang, et al. "FC 017DEEP-LEARNING ENABLED QUANTIFICATION OF SINGLE-CELL SINGLE-MRNA TRANSCRIPTS AND CORRELATIVE SUPER-RESOLVED PODOCYTE FOOT PROCESS MORPHOMETRY IN ROUTINE KIDNEY BIOPSY SPECIMEN." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab138.003.
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Abstract Background and Aims Although high-throughput single-cell transcriptomic analysis, super-resolution light microscopy and deep-learning methods are broadly used, the gold-standard to evaluate kidney biopsies is still the histologic assessment of formalin-fixed and paraffin embedded (FFPE) samples with parallel ultrastructural evaluation. Recently, we and others have shown that super-resolution fluorescence microscopy can be used to study glomerular ultrastructure in human biopsy samples. Additionally, in the last years mRNA in situ hybridization techniques have been improved to increase specificity and sensitivity to enable transcriptomic analysis with single-mRNA resolution (smFISH). Method For smFISH, we used the fluorescent multiplex RNAscope kit with probes targeting ACE2, WT1, PPIB, UBC and POLR2A. To find an on-slide reference gene, the normfinder algorithm was used. The smFISH protocol was combined with a single-step anti-podocin immunofluorescence enabled by VHH nanobodies. Podocytes were labeled by tyramide-signal amplified immunofluorescence using recombinant anti-WT1 antibodies. Slides were imaged using confocal laser scanning, as well as 3D structured illumination microscopy. Deep-learning networks to segment glomeruli and cell nuclei (UNet and StarDist) were trained using the ZeroCostDL4Mic approach. Scripts to automate analysis were developed in the ImageJ1 macro language. Results First, we show robust functionality of threeplex smFISH in archived routine FFPE kidney biopsy samples with single-mRNA resolution. As variations in sample preparation can negatively influence mRNA-abundance, we established PPIB as an ideal on-slide reference gene to account for different RNA-integrities present in biopsy samples. PPIB was chosen for its most stable expression in microarray dataset of various glomerular diseases determined by the Normfinder algorithm as well as its smFISH performance. To segment glomeruli and to label glomerular and tubulointerstitial cell subsets, we established a combination of smFISH and immunofluorescence. As smFISH requires intense tissue digestion to liberate cross-linked RNAs, immunofluorescence protocols had to be adapted: For podocin, a small-sized single-step label approach enabled by small nanobodies and for WT1, tyramide signal amplification was used. For enhanced segmentation performance, we used deep learning: First, a network was customized to recognize DAPI+ cell nuclei and WT1/DAPI+ podocyte nuclei. Second, a UNet was trained to segment glomeruli in podocin-stained tissue sections. Using these segmentation masks, we could annotate PPIB-normalized single mRNA transcripts to individual cells. We established an ImageJ script to automatize transcript quantification. As a proof-of-principle, we demonstrate inverse expression of WT1 and ACE2 in glomerular vs. tubulointerstitial single cells. Furthermore, in the podocyte subset, WT1 highly clustered whereas no significant ACE2 expression was found under baseline conditions. Additionally, when imaged with super-resolution microscopy, podocyte filtration slit morphology could be visualized The optical resolution was around 125 nm and therefore small enough to resolve individual foot processes. The filtration slit density as a podocyte-integrity marker did not differ significantly from undigested tissue sections proving the suitability for correlative podocyte foot process morphometry with single-podocyte transcript analysis. Conclusion Here we present a modular toolbox which combines algorithms for multiplexed, normalized single-cell gene expression with single mRNA resolution in cellular subsets (glomerular, tubulointerstitial and podocytes). Additionally, this approach enables correlation with podocyte filtration slit ultrastructure and gross glomerular morphometry.
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Selvaskandan, Haresh, Charlotte Boys, Izabella Pawluczyk, and Jonathan Barratt. "#3979 DIGITAL SPATIAL PROFILING CAN BE USED TO STUDY GLOMERULAR ENDOTHELIAL CELLS IN IGA NEPHROPATHY." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063c_3979.
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Abstract Background and Aims IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide; approximately 30% of cases progress to kidney failure 10-20 years from diagnosis. Five histopathological kidney lesions independently predict a poor prognosis in IgAN (MEST-C score) [1]. Published case series highlight the ‘endocapillary hypercellularity’ (E1) lesion as potentially reversible with systemic immunosuppression, improving clinical outcomes [2]. Delineating differences in the transcriptomes of glomerular endothelial cells (GEnCs) associated with and without E1 (E0) may highlight avenues for safer therapeutic strategies, given the overt risks associated with systemic immunosuppression in IgAN. GEnC transcriptomes have never been profiled in diseased kidneys before. Here we used digital spatial profiling (DSP) to achieve this. Method DSP was performed on a Nanostring GeoMx platform. Single 5μm sections were collected from four formalin fixed paraffin embedded (FFPE) kidney biopsies with E1 and five with E0. Following deparaffinization and antigen retrieval, the tissue was incubated with a whole transcriptome atlas probe set. GEnCs were stained for CD31 (red) and macrophages (which associate with E1[3]) with CD68 (yellow) with conjugated antibodies. Glomeruli were selected as regions of interests (ROIs), and a custom JavaScript function was used to mask over GEnCs and macrophages (segmentation), which were selected as areas of illumination (AOIs) (Fig 1). Photocleaved nucleotide barcodes were sequenced using an Illumina sequencer. Single cell enrichment was assessed using the SpatialDecon algorithm, differential gene expression was explored using a linear mixed effects model, and pathway analysis was performed using Reactome. Results The custom written JavaScript function allowed good segmentation on GEnCs and macrophages (Fig 1). Single cell deconvolution performed using the human kidney cell atlas as a reference showed significant enrichment of GEnCs relative to neighbouring cell types (Fig 2). Exploration of differential gene expressions using a linear mixed effects model found an up-regulation of TRIM23, IL27RA, TMEM139, P14K2B and PSMD9, after P value adjustment, among GEnCs associated with macrophages in glomerular capillary loops compared to those in the absence of macrophages (Fig 3). Pathway analysis based on differential gene expression performed using Reactome revealed that the complement cascade and regulators of the complement cascade were enhanced in GEnCs associated with macrophages (Fig 4). Conclusion This pilot study found DSP on the GeoMx to be effective at enriching GEnC transcript signals from neighbouring cell types in FFPE tissue. This preliminary data also shows that GEnCs associated with macrophages may display a more inflammatory phenotype, which may be related to up-regulation in complement activity and may account for the progressive phenotype associated with E1 in IgAN. With several trials investigating complement system targeting therapeutics in IgAN, validation of these findings might highlight a cohort of patients with IgAN most likely to benefit from treatment.
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Schmitz, Jessica, David Christensen, Lena Müller, et al. "MO055: Multi-Class Segmentation of Kidney Tissues using Convolutional Neuronal Networks (CNNS)." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac063.007.
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Abstract BACKGROUND AND AIMS Routine pathological diagnostics in kidneys are mainly based on semi-quantitative eyeballing. In own former studies, we showed predictive value of precise immune cell quantification in allografts using digital semi-automated techniques. We now aim to achieve fully automated segmentation workflow with CNNs. METHOD Standard routine stains (immuno/histochemistry, immunofluorescence) were digitized (20×) with Metafer, a commercial scanning/imaging platform. Diagnostically relevant anatomical compartments (cortex, medulla, glomeruli, tubuli [proximal/distal/collecting duct], glomerular/peritubular capillaries and nuclei) were manually annotated by use of immunomarkers to generate large data sets on human renal biopsies and nephrectomies. Data were used to train multi-class semantic segmentation CNNs with broad data augmentation to achieve a robustness against staining variances. RESULTS Using Jones-HE stains for multi-class segmentation, a cortex-medulla-extrarenal CNN revealed pixel based hit rates above 97.9%, detection of glomeruli had a pixel based hit rate above 99%, a multi-class CNN for tubules, tubular membranes and peritubular capillaries resulted in a hit rate of 91.5%, and nuclear-based cell detection shows pixel based hit rates above 98%. Identification of cell location in interstitium, tubuli, glomeruli, peritubular and glomerular capillaries reached very high hit rates: Glomerular endothelial cells actually result in 83% true positives, 13% false negatives and 4% false positives. Additionally, a tubulus classifier (proximal tubulus, distal tubulus, collecting duct and atrophic tubulus) with an accuracy &gt;90% was developed. CONCLUSION Automated structure segmentation by CNNs can complement and specify classical nephropathological diagnostics, especially for spatial risk marker evaluation in early transplant biopsies.
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Martínez Mora, Andrés, Elin Lundström, Taro Langner, et al. "MO621AGE-RELATED PATTERNS OF KIDNEY PARENCHIMAL VOLUME IN T1D, T2D AND DIFFERENT TREATMENT GROUPS OF T2D: A CROSS-SECTIONAL STUDY OF 35,703 UK BIOBANK PARTICIPANTS." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab093.002.
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Abstract Background and Aims Kidney parenchymal volume (KPV) presents a natural variation with respect to sex, age, and body size, and is also affected by diseases such as diabetes. The UK Biobank (UKBB) is a large-scale study including clinical and MRI data. The current project investigated the association between KPV and age in UKBB participants without diabetes and with diabetes type 1 (T1D), and type 2 (T2D). In addition, the effect of different treatments for T2D on KPV was investigated. Method KPV was estimated in 35,703 UKBB participants (52% women, age = 45-82 years) with a deep-learning-based segmentation of both kidneys (Dice = 0.956, error &lt; 4%). The cohort was classified into Control, T1D and T2D subjects using an algorithm developed on UKBB clinical data (Eastwood et al 2016). Individuals with T2D were further divided into groups related to treatment: Lifestyle (no pharmaceuticals, i.e. light treatment, mean disease duration of 6.2 years), Metformin (metformin as the only pharmaceutical, i.e. intermediate treatment, mean disease duration of 8.3 years), and Other (more potent treatment, combination of pharmaceuticals, mean disease duration of 14.1 years). KPV was studied as a function of age in the different groups, divided according to sex. The statistical difference in mean KPV between groups was tested. For each group, the association between KPV and age was assessed by linear regression. Comparison of line slopes was conducted to investigate whether age-related patterns in KPV differed statistically between groups. Results The moving average curve of KPV vs age, in controls and subjects with T1D and T2D, is shown in Fig 1A (with a 15-year sliding window). The corresponding curve for different T2D treatment groups is depicted in Fig 1B. Fig 2A-D presents results for the comparison of KPV and regression line slope between the subject groups. According to Fig 1A and 2A, KPV is usually higher in subjects with T1D and T2D than in controls. As shown in Fig 1B and 2B, T2D subjects with longer disease duration and on pharmaceutical treatment (Metformin or Other) are generally more prone to large KPV than subjects with adapted lifestyle as treatment. The decreasing KPV pattern with age is faster in T2D subjects than controls but not significantly different between T1D subjects and controls (Fig 1A and 2C). Women in group Other also show a pattern of steeper age-related decline in KPV compared to remaining women with T2D treatment (Fig 1B and 2D). Conclusion Compared to controls, T1D subjects show enlarged KPV similar to that of T2D subjects, which is in line with previous literature. Subjects with T2D show a pattern of steeper age-related decline in KPV compared to controls. Female T2D subjects with longer disease duration, usually on a more potent treatment (beyond adaption of lifestyle and metformin as the only pharmaceutical), are more prone to enlarged KPV and exhibit a pattern of steeper age-related decline in KPV. This may be due to hyperfiltration caused by diabetes, resulting in increased kidney size. The normal loss of glomeruli with age could be accelerated by diabetes, leading to a greater loss in KPV per year in diabetics. Steeper KPV decline patterns in disease could also be caused by selection bias where old subjects with large KPV and related complications are less likely to participate in the study.
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Kuo, Willy, Diego Rossinelli, Georg Schulz, et al. "Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney." Scientific Data 10, no. 1 (2023). http://dx.doi.org/10.1038/s41597-023-02407-5.
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AbstractThe performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.