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Статті в журналах з теми "Lesion detection"
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Zrzavy, Tobias, Alice Wielandner, Lukas Haider, Sophie Bartsch, Fritz Leutmezer, Thomas Berger, Karl Heinz Nenning, Alexander Rauscher, Paulus Rommer, and Gregor Kasprian. "FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols." Journal of Neurology 269, no. 1 (October 8, 2021): 461–67. http://dx.doi.org/10.1007/s00415-021-10833-x.
Повний текст джерелаАнотація:
Abstract Background Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR2) results in isovoxel images with increased contrast-to-noise ratio, increased white–gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR2 in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions. Objective To compare lesion detection of 3D-FLAIR2 with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images. Methods We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR2 scans using the ITK-SNAP tool. Results LC lesion visibility was significantly improved by 3D-FLAIR2 in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR2 vs. 3D-FLAIR, 3D-FLAIR2 detected on average 3.2 more cortical lesions (95% CI − 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR2 detected on average 3.7 more LC lesions (95% CI 3.3–10.7). Conclusions 3D-FLAIR2 is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR2.
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Correia, J., A. Ponte, L. Proença, A. Rodrigues, R. Pinho, S. Leite, C. Fernandes, et al. "P206 Comparison of dye-spraying chromoendoscopy and virtual chromoendoscopy for colonic dysplasia detection in longstanding Inflammatory Bowel Disease." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i265. http://dx.doi.org/10.1093/ecco-jcc/jjab232.333.
Повний текст джерелаАнотація:
Abstract Background Dye-spraying chromoendoscopy (DCE) is the technique of choice for colonic surveillance in patients with long-standing extensive Inflammatory Bowel Disease (IBD). Conversely, the use of virtual chromoendoscopy (VCE) is still controversial. This study aimed to compare lesion and dysplasia detection and accuracy of margins delineation between VCE and DCE. Methods Eleven gastroenterologists were given a survey with 20 pairs of pictures of IBD surveillance colonoscopies (10 with nondysplastic lesions, 5 with dysplastic lesions and 5 with no lesions). Each pair contained the same image captured during colonoscopy using VCE and DCE. The 40 pictures were randomly ordered to avoid any classification bias. For each picture, the gastroenterologist assessed the presence/absence of lesion and, when a lesion was identified, assessed the presence/absence of dysplasia and delineated its margins. To compare lesion and dysplasia detections between techniques, the sensitivity, specificity and inter-observer agreement (using fleiss’ kappa (K) test) were calculated. The chi-square test was used to assess the accuracy of margins delineation. Results When assessing lesion detection using VCE, sensitivity (S) and specificity (E) were 0.93 and 0.49 and in, DCE, 0.97 and 0.38, respectively. When assessing dysplasia detection using VCE, S and E were 0.74 and 0.60 and, in DCE, 0.67 and 0.62, respectively. Interobserver agreement analysis revealed that VCE and DCE had a moderate agreement in lesion detection - 0.57 and 0.58, respectively; however, for dysplasia detection, VCE had a fair agreement (k=0.30) and DCE a slight agreement (k=0.11). The rate of accurately defined margins was similar for both techniques (p=0.22). Conclusion Similar lesion and dysplasia detection and margins delineation were achieved with both techniques. However, concerning dysplasia detection, interobserver agreement was slightly better using VCE. Therefore, VCE may constitute a valid alternative to DCE for dysplasia screening in IBD. Further studies are needed to validate these findings.
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Wattjes, Mike P., Martijn T. Wijburg, Anke Vennegoor, Birgit I. Witte, Stefan D. Roosendaal, Esther Sanchez, Yaou Liu, et al. "Diagnostic performance of brain MRI in pharmacovigilance of natalizumab-treated MS patients." Multiple Sclerosis Journal 22, no. 9 (July 20, 2016): 1174–83. http://dx.doi.org/10.1177/1352458515615225.
Повний текст джерелаАнотація:
Background: In natalizumab-treated multiple sclerosis (MS) patients, magnetic resonance imaging (MRI) is considered as a sensitive tool in detecting both MS disease activity and progressive multifocal leukoencephalopathy (PML). Objective: To investigate the performance of neuroradiologists using brain MRI in detecting new MS lesions and asymptomatic PML lesions and in differentiating between MS and PML lesions in natalizumab-treated MS patients. The secondary aim was to investigate interrater variability. Methods: In this retrospective diagnostic study, four blinded neuroradiologists assessed reference and follow-up brain MRI scans of 48 natalizumab-treated MS patients with new asymptomatic PML lesions ( n = 21) or new MS lesions ( n = 20) or no new lesions ( n = 7). Sensitivity and specificity for detection of new lesions in general (MS and PML lesions), MS and PML lesion differentiation, and PML detection were determined. Interrater agreement was calculated. Results: Overall sensitivity and specificity for the detection of new lesions, regardless of the nature of the lesions, were 77.4% and 89.3%, respectively; for PML-MS lesion differentiation, 74.2% and 84.7%, respectively; and for asymptomatic PML lesion detection, 59.5% and 91.7%, respectively. Interrater agreement for the tested categories was fair to moderate. Conclusion: The diagnostic performance of trained neuroradiologists using brain MRI in pharmacovigilance of natalizumab-treated MS patients is moderately good. Interrater agreement among trained readers is fair to moderate.
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Metser, Ur, Orit Golan, Charles D. Levine, and Einat Even-Sapir. "Tumor Lesion Detection." Journal of Computer Assisted Tomography 29, no. 4 (July 2005): 554–59. http://dx.doi.org/10.1097/01.rct.0000164671.96143.c2.
Повний текст джерела
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Bhardwaj, Charu, Shruti Jain, and Meenakshi Sood. "Automated Diagnostic Hybrid Lesion Detection System for Diabetic Retinopathy Abnormalities." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 494–507. http://dx.doi.org/10.2174/2210327909666191126092411.
Повний текст джерелаАнотація:
Background: Early diagnosis, monitoring disease progression, and timely treatment of Diabetic Retinopathy (DR) abnormalities can efficiently prevent visual loss. A prediction system for the early intervention and prevention of eye diseases is important. The contrast of raw fundus image is also a hindrance in effective manual lesion detection technique. Methods: In this research paper, an automated lesion detection diagnostic scheme has been proposed for early detection of retinal abnormalities of red and yellow pathological lesions. The algorithm of the proposed Hybrid Lesion Detection (HLD) includes retinal image pre-processing, blood vessel extraction, optical disc localization and detection stages for detecting the presence of diabetic retinopathy lesions. Automated diagnostic systems assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. Detailed statistical analysis is performed on the extracted shape, intensity and GLCM features and the optimal features are selected to classify DR abnormalities. Exhaustive statistical investigation of the proposed approach using visual and empirical analysis resulted in 31 significant features. Results: The results show that the HLD approach achieved good classification results in terms of three statistical indices: accuracy, 98.9%; sensitivity, 97.8%; and specificity, 100% with significantly less complexity. Conclusion: The proposed technique with optimal features demonstrates improvement in accuracy as compared to state of the art techniques using the same database.
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Kuklyte, Jogile, Jenny Fitzgerald, Sophie Nelissen, Haolin Wei, Aoife Whelan, Adam Power, Ajaz Ahmad, et al. "Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies." Toxicologic Pathology 49, no. 4 (February 23, 2021): 815–42. http://dx.doi.org/10.1177/0192623320986423.
Повний текст джерелаАнотація:
Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.
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Matsui, Hiroaki, Shunsuke Kamba, Hideka Horiuchi, Sho Takahashi, Masako Nishikawa, Akihiro Fukuda, Aya Tonouchi, et al. "Detection Accuracy and Latency of Colorectal Lesions with Computer-Aided Detection System Based on Low-Bias Evaluation." Diagnostics 11, no. 10 (October 17, 2021): 1922. http://dx.doi.org/10.3390/diagnostics11101922.
Повний текст джерелаАнотація:
We developed a computer-aided detection (CADe) system to detect and localize colorectal lesions by modifying You-Only-Look-Once version 3 (YOLO v3) and evaluated its performance in two different settings. The test dataset was obtained from 20 randomly selected patients who underwent endoscopic resection for 69 colorectal lesions at the Jikei University Hospital between June 2017 and February 2018. First, we evaluated the diagnostic performances using still images randomly and automatically extracted from video recordings of the entire endoscopic procedure at intervals of 5 s, without eliminating poor quality images. Second, the latency of lesion detection by the CADe system from the initial appearance of lesions was investigated by reviewing the videos. A total of 6531 images, including 662 images with a lesion, were studied in the image-based analysis. The AUC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.983, 94.6%, 95.2%, 68.8%, 99.4%, and 95.1%, respectively. The median time for detecting colorectal lesions measured in the lesion-based analysis was 0.67 s. In conclusion, we proved that the originally developed CADe system based on YOLO v3 could accurately and instantaneously detect colorectal lesions using the test dataset obtained from videos, mitigating operator selection biases.
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Rezzo, R., G. Scopinaro, M. Gambaro, P. Michetti, and G. Anfossi. "Radioguided Occult Colonic Lesion Identification (Rocli) during Open and Laparoscopic Surgery." Tumori Journal 88, no. 3 (May 2002): S19—S22. http://dx.doi.org/10.1177/030089160208800328.
Повний текст джерелаАнотація:
Aims and Background Intraoperative localization, during open and laparoscopic surgery, of small, nonpalpable colonic lesions located at peculiar sites or with concurrent inflammatory bowel alterations (diverticulosis, perivisceritis) is often difficult. The aim of our work was to assess the validity of radioguided identification after preoperative labeling. Methods and Study Design Patients who were candidates for colon surgery for occult lesions that, because of their size and location, were assumed to be difficult to detect, underwent colonoscopy 1 to 2.5 hours before surgery. A small dose of labeled albumin macroaggregates was injected with a sclerotherapy needle into the subserosa underneath the lesion. Immediately following injection the lesion was identified with a transcutaneously placed gamma detecting probe. Intraoperative tracer detection was performed either during open surgery or by means of a laparoscopic probe (detection time 3-5 mins). The position of the lesion was marked with a suture or with a clip. Surgery was performed according to the type of lesion to be treated. Results In our initial clinical experience 15 colon lesions were preoperatively marked in 14 patients and were subsequently detected during surgery (four under laparoscopy) with a gamma detecting probe. This technique allows highly accurate, fast, and inexpensive surgical localization of lesions without irradiation and without complications. Conclusion Our experience shows that preoperative endoscopic marking of nonpalpable colon lesions with 99mTc-labeled albumin macroaggregates followed by intraoperative detection with a gamma probe is a useful clinical method that is highly accurate and without complications.
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Sen Saxena, Vivek, Prashant Johri, and Avneesh Kumar. "AI-Enabled Support System for Melanoma Detection and Classification." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 58–75. http://dx.doi.org/10.4018/ijrqeh.2021100104.
Повний текст джерелаАнотація:
Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.
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O'Brien, W. J., L. Vazquez, and W. M. Johnston. "The Detection of Incipient Caries with Tracer Dyes." Journal of Dental Research 68, no. 2 (February 1989): 157–58. http://dx.doi.org/10.1177/00220345890680021101.
Повний текст джерелаАнотація:
The purpose of this study was to determine the increase in color contrast produced by the use of a tracer dye in detection of incipient caries lesions with transillumination. Twenty four caries-free first premolars were immersed in an acid gelatin for production of artificial incipient caries lesions. After the lesions had developed, these teeth were photographed by transillumination. Two photographs were taken of each tooth. The first photograph showed the lesion without dye. A blue tracer dye was then added and absorbed by the lesion, and a second photograph was taken. The data on the color difference were obtained by use of a reflectance colorimeter and showed a four-fold increase between the lesion and surrounding area with the dye. A two-way analysis of variance was used for the statistical interpretation. The color difference between the lesion without the dye and then with the dye was significant. The use of the blue tracer dye, therefore, significantly increased the contrast in the images of the artificial incipient lesions.
Дисертації з теми "Lesion detection"
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Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.
Повний текст джерелаАнотація:
Malignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of the main keys that refer to melanoma or non-melanoma disease. However, determining these clinical features can be a time-consuming, subjective (even for trained clinicians) and challenging task for several reasons: lesions vary considerably in size and colour, low contrast between an affected area and the surrounding healthy skin, especially in early stages, and the presence of several elements such as hair, reflections, oils and air bubbles on almost all images. This thesis aims to provide an accurate, robust and reliable automated dermoscopy image analysis technique, to facilitate the early detection of malignant melanoma disease. In particular, four innovative methods are proposed for region segmentation and classification, including two for pigmented region segmentation, one for pigment network detection, and one for lesion classification. In terms of boundary delineation, four pre-processing operations, including Gabor filter, image sharpening, Sobel filter and image inpainting methods are integrated in the segmentation approach to delete unwanted objects (noise), and enhance the appearance of the lesion boundaries in the image. The lesion border segmentation is performed using two alternative approaches. The Fuzzy C-means and the Markov Random Field approaches detect the lesion boundary by repeating the labeling of pixels in all clusters, as a first method. Whereas, the Particle Swarm Optimization with the Markov Random Field method achieves greater accuracy for the same aim by combining them in the second method to perform a local search and reassign all image pixels to its cluster properly. With respect to the pigment network detection, the aforementioned pre-processing method is applied, in order to remove most of the hair while keeping the image information and increase the visibility of the pigment network structures. Therefore, a Gabor filter with connected component analysis are used to detect the pigment network lines, before several features are extracted and fed to the Artificial Neural Network as a classifier algorithm. In the lesion classification approach, the K-means is applied to the segmented lesion to separate it into homogeneous clusters, where important features are extracted; then, an Artificial Neural Network with Radial Basis Functions is trained by representative features to classify the given lesion as melanoma or not. The strong experimental results of the lesion border segmentation methods including Fuzzy C-means with Markov Random Field and the combination between the Particle Swarm Optimization and Markov Random Field, achieved an average accuracy of 94.00% , 94.74% respectively. Whereas, the lesion classification stage by using extracted features form pigment network structures and segmented lesions achieved an average accuracy of 90.1% , 95.97% respectively. The results for the entire experiment were obtained using a public database PH2 comprising 200 images. The results were then compared with existing methods in the literature, which have demonstrated that our proposed approach is accurate, robust, and efficient in the segmentation of the lesion boundary, in addition to its classification.
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Rolland, Jannick Paule Yvette. "Factors influencing lesion detection in medical imaging." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185096.
Повний текст джерелаАнотація:
An important goal in medical imaging is the assessment of image quality in a way that relates to clinical efficacy. An objective approach is to evaluate the performance of diagnosis for specific tasks, using ROC analysis. We shall concentrate here on classification tasks. While many factors may confine the performance achieved for these tasks, we shall investigate two main limiting factors: image blurring and object variability. Psychophysical studies followed by ROC analysis are widely used for system assessment, but it is of great practical interest to be able to predict the outcome of psychophysical studies, especially for system design and optimization. The ideal observer is often chosen as a standard of comparison for the human observer since, at least for simple tasks, its performance can be readily calculated using statistical decision theory. We already know, however, of cases reported in the literature where the human observer performs far below ideal, and one of the purposes of this dissertation is to determine whether there are other practical circumstances where human and ideal performances diverge. Moreover, when the complexity of the task increases, the ideal observer becomes quickly intractable, and other observers such as the Hotelling and the nonprewhitening (npw) ideal observers may be considered instead. A practical problem where our intuition tells us that the ideal observer may fail to predict human performance occurs with imaging devices that are characterized by a PSF having long spatial tails. The investigation of the impact of long-tailed PSFs on detection is of great interest since they are commonly encountered in medical imaging and even more generally in image science. We shall show that the ideal observer is a poor predictor of human performance for a simple two-hypothesis detection task and that linear filtering of the images does indeed help the human observer. Another practical problem of considerable interest is the effect of background nonuniformity on detectability since, it is one more step towards assessing image quality for real clinical images. When the background is known exactly (BKE), the Hotelling and the npw ideal observers predict that detection is optimal for an infinite aperture; a spatially varying background (SVB) results in an optimum aperture size. Moreover, given a fixed aperture size and a BKE, an increase in exposure time is highly beneficial for both observers. For SVB, on the other hand, the Hotelling observer benefits from an increases in exposure time, while the npw ideal observer quickly saturates. In terms of human performance, results show a good agreement with the Hotelling-observer predictions, while the performance disagrees strongly with the npw ideal observer.
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Nagane, Radhika. "Detection of flash in dermoscopy skin lesion images." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Nagane_09007dcc803ec3f9.pdf.
Повний текст джерелаАнотація:
Thesis (M.S.)--University of Missouri--Rolla, 2007.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007) Includes bibliographical references (p. 89-90).
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Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.
Повний текст джерелаАнотація:
This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly, simplifying the traditional cancer detection pipeline. Secondly, different initialization proposals by means of reducing the human interaction in a lesion segmentation algorithm based on Markov Random Field (MRF)-Maximum A Posteriori (MAP) framework is presented. Furthermore, an analysis of the influence of lesion type in the segmentation results is performed. Finally, the inclusion of elastography information in this segmentation framework is proposed, by means of modifying the algorithm to incorporate a bivariant formulation. The proposed methods in the different stages of the CAD framework are assessed using different datasets, and comparing the results with the most relevant methods in the state-of-the-artAquesta tesi es centra en la detecció, segmentació i classificació de lesions en imatges d'ecografia. La contribució d'aquesta tesi és el desenvolupament d'una nova eina de Diagnòstic Assistit per Ordinador (DAO) capaç de detectar, segmentar i classificar automàticament lesions en imatges d'ecografia de mama. Inicialment, s'ha proposat l'adaptació del mètode genèric de detecció d'objectes Deformable Part Models (DPM) per detectar lesions en imatges d'ecografia. Aquest mètode utilitza tècniques d'aprenentatge automàtic per generar un model basat en l'Histograma de Gradients Orientats. Aquest mètode també és utilitzat per detectar lesions malignes directament, simplificant així l'estratègia tradicional. A continuació, s'han realitzat diferents propostes d'inicialització en un mètode de segmentació basat en Markov Random Field (MRF)-Maximum A Posteriori (MAP) per tal de reduir la interacció amb l'usuari. Per avaluar aquesta proposta, s'ha realitzat un estudi sobre la influència del tipus de lesió en els resultats aconseguits. Finalment, s'ha proposat la inclusió d'elastografia en aquesta estratègia de segmentació. Els mètodes proposats per a cada etapa de l'eina DAO han estat avaluats fent servir bases de dades diferents, comparant els resultats obtinguts amb els resultats dels mètodes més importants de l'estat de l'art
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Gonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.
Повний текст джерелаАнотація:
Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.
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Gomez, Bulla Juliana. "Detection, diagnosis and management of the early carious lesion." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/detection-diagnosis-and-management-of-the-early-carious-lesion(f7ae030d-fe41-4e3d-802a-a3cd8c0e978d).html.
Повний текст джерелаАнотація:
The current evidenced-based caries understanding, based on biological concepts, involves new approaches in caries detection, assessment, and management that should include non-cavitated lesions. The purpose of the studies presented in this thesis was to investigate the current available evidence on methods to detect non-cavitated lesions (NCCls), the current evidence related to the efficacy of non-surgical caries preventive methods to arrest or reverse the progression of NCCls, the current evidence for the prediction of caries using four caries risk assessment systems/guidelines and a review of the literature related to alternative caries clinical trial methods for oral care products. The purpose of the in vitro studies was to study the performance of different caries detection methods (ICDAS, ICDAS photographs, FOTI, QLF, OCT, Soprolife) in detecting early caries lesions and in particular and to assess the QLF ability to detect changes after remineralisation/demineralisation cycles. The last study was a cross-sectional study aiming to investigate the caries management decisions for early caries lesions among dentists. The results of the systematic reviews (Paper I-IV) suggest a large variation of Sensitivity, Specificity and lack of consistence on the definition of disease among the detection methods assessed. The evidence on Caries Risk Assessment Systems is limited and the current systems seem not to predict future disease. In terms of Caries Management, according to the evidence fluorides continue to be the most effectiveness anti-caries agent. The evidence on abbreviated clinical trials showed excellent discrimination between anti-caries products in short clinical trials with fewer subjects using more sensitive caries detection methods. Paper V, showed that all the caries detection methods assessed in this study, except for OCT (0.65), were strongly correlated with Histology. In papers VI and VII, QLF showed the ability to detect differences between two NaF toothpastes (550 ppm F, 1100 ppm F) and a fluoride placebo treatment in two pH cycling models. Finally, the results of the questionnaire on Caries Related Treatment Decisions (Paper VIII) revealed that 60% of the dentists are practising prevention in occlusal early lesions. However, a large number of dentists are still oriented towards a restorative approach and do not base their treatment decisions on individual caries risk. The main conclusions from this thesis are that: 1) A comprehensive management system should include initial caries lesions; 2) Visual examinations is still the standard method of detection, other methods may be included for monitoring purposes; 3) QLF was able to detect remineralisation of artificial carious lesions and inhibition of demineralisation in sound enamel after two remineralisation/demineralisation pH cycling models; 4) The results of the cross-over study indicate that Colombian dentists have not yet fully adopted conservative treatment for early caries lesions.
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Agarwal, Richa. "Computer aided detection for breast lesion in ultrasound and mammography." Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/670295.
Повний текст джерелаАнотація:
In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM).
Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis.
Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging.
In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used.
The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screeningEn el camp de les imatges de càncer de mama, els sistemes tradicionals de detecció assistida per ordinador (de l’anglès CAD) es van dissenyar utilitzant recursos informàtics limitats i pel·lícules de mamografia escanejades (del angles SFM) de qualitat d’imatge deficient, fet que va resultar en aplicacions poc robustes. Actualment, amb els avanços de les tecnologies, és possible realitzar imatges mèdiques en 3D i adquirir mamografies digitals (de l’anglès FFDM) d’alta qualitat. L’ultrasò automàtic de la mama (de l’anglès ABUS) ha estat proposat per adquirir imatges 3D de la mama amb escassa dependència del operador. Quan s’utilitza ABUS, la segmentació i seguiment de les lesions en el temps s ́on tasques complicades ja que la naturalesa 3D de les imatges fa que l’anàlisi sigui difícil i feixuc per els radiòlegs. Un dels objectius d’aquesta tesi és desenvolupar un marc per la segmentació semi-automàtica de lesions mamàries en volums ABUS. El volum de lesió 3D, en combinació amb l’anàlisi de la textura i el contorn, podria proporcionar informació valuosa per realitzar el diagnòstic radiològic. Tot i que els volums de ABUS són de gran interès, la mamografia de raigs X continua essent la modalitat d’imatge estàndard utilitzada per la detecció precoç del càncer de mama, degut principalment a la seva ràpida adquisició i rendibilitat. A més, amb l’arribada dels mètodes d’aprenentatge profund basats en xarxes neuronals convolucionals (de l’anglès CNN), els sistemes CAD moderns poden aprendre automàticament quines característiques de la imatge són més rellevants per realitzar un diagnòstic, fet que augmenta la utilitat d’aquests sistemes. Una de les limitacions de les CNN és que requereixen de grans conjunts de dades per entrenar, els quals són molt limitats en el camp de la imatge mèdica. En aquesta tesi, el tema de la poca disponibilitat d’imatges mediques s’aborda mitjançant dues estratègies: (i) utilitzant regions de la imatge com a entrada en comptes de les imatges de mida original, i (ii) mitjançant tècniques d’aprenentatge per transferència, en el que el coneixement après per a una determinada tasca es transfereix a una altra tasca relacionada (també conegut com a adaptació de domini). En primer lloc, la CNN entrenada en un conjunt de dades molt gran d’imatges naturals és adaptada per classificar regions de la imatge en tumor i no tumor de SFM i, en segon lloc, la CNN entrenada és adaptada per detectar tumors en FFDM. També s’ha investigat l’aprenentatge per transferència entre imatges naturals i FFDM. S’han utilitzat dos conjunts de dades públiques (CBIS-DDSM i INbreast) per aquest propòsit. En la fase final de la investigació, es proposa un marc de detecció automàtica de tumors utilitzant la mamografia original com entrada (en lloc de regions de la imatge) i que proporciona la localització de la lesió dins d’aquesta mamografia com a sortida. Per aquest propòsit s’utilitza una altra base de dades (OMI-DB). Els resultats obtinguts com a part d’aquesta tesi mostren millors rendiments en comparació amb l’estat de l’art, el que indica que els mètodes i marcs proposats tenen el potencial de ser implementats dins de sistemes CAD avançats, que poden ser utilitzats per radiòlegs en el cribratge del càncer de mama
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Yap, Moi Hoon. "Enhanced algorithms for lesion detection and recognition in ultrasound breast images." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35018.
Повний текст джерелаАнотація:
Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance.
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Alaverdyan, Zaruhi. "Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI005/document.
Повний текст джерелаАнотація:
Cette étude vise à développer un système d’aide au diagnostic (CAD) pour la détection de lésions épileptogènes, reposant sur l’analyse de données de neuroimagerie, notamment, l’IRM T1 et FLAIR. L’approche adoptée, introduite précédemment par Azami et al., 2016, consiste à placer la tâche de détection dans le cadre de la détection de changement à l'échelle du voxel, basée sur l’apprentissage d’un modèle one-class SVM pour chaque voxel dans le cerveau. L'objectif principal de ce travail est de développer des mécanismes d’apprentissage de représentations, qui capturent les informations les plus discriminantes à partir de l’imagerie multimodale. Les caractéristiques manuelles ne sont pas forcément les plus pertinentes pour la tâche visée. Notre première contribution porte sur l'intégration de différents réseaux profonds non-supervisés, pour extraire des caractéristiques dans le cadre du problème de détection de changement. Nous introduisons une nouvelle configuration des réseaux siamois, mieux adaptée à ce contexte. Le système CAD proposé a été évalué sur l’ensemble d’images IRM T1 des patients atteints d'épilepsie. Afin d'améliorer la performance obtenue, nous avons proposé d'étendre le système pour intégrer des données multimodales qui possèdent des informations complémentaires sur la pathologie. Notre deuxième contribution consiste donc à proposer des stratégies de combinaison des différentes modalités d’imagerie dans un système pour la détection de changement. Ce système multimodal a montré une amélioration importante sur la tâche de détection de lésions épileptogènes sur les IRM T1 et FLAIR. Notre dernière contribution se focalise sur l'intégration des données TEP dans le système proposé. Etant donné le nombre limité des images TEP, nous envisageons de synthétiser les données manquantes à partir des images IRM disponibles. Nous démontrons que le système entraîné sur les données réelles et synthétiques présente une amélioration importante par rapport au système entraîné sur les images réelles uniquementThis work represents one attempt to develop a computer aided diagnosis system for epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR MR sequences. Given the complexity of the task and the lack of a representative voxel-level labeled data set, the adopted approach, first introduced in Azami et al., 2016, consists in casting the lesion detection task as a per-voxel outlier detection problem. The system is based on training a one-class SVM model for each voxel in the brain on a set of healthy controls, so as to model the normality of the voxel. The main focus of this work is to design representation learning mechanisms, capturing the most discriminant information from multimodality imaging. Manual features, designed to mimic the characteristics of certain epilepsy lesions, such as focal cortical dysplasia (FCD), on neuroimaging data, are tailored to individual pathologies and cannot discriminate a large range of epilepsy lesions. Such features reflect the known characteristics of lesion appearance; however, they might not be the most optimal ones for the task at hand. Our first contribution consists in proposing various unsupervised neural architectures as potential feature extracting mechanisms and, eventually, introducing a novel configuration of siamese networks, to be plugged into the outlier detection context. The proposed system, evaluated on a set of T1-weighted MRIs of epilepsy patients, showed a promising performance but a room for improvement as well. To this end, we considered extending the CAD system so as to accommodate multimodality data which offers complementary information on the problem at hand. Our second contribution, therefore, consists in proposing strategies to combine representations of different imaging modalities into a single framework for anomaly detection. The extended system showed a significant improvement on the task of epilepsy lesion detection on T1-weighted and FLAIR MR images. Our last contribution focuses on the integration of PET data into the system. Given the small number of available PET images, we make an attempt to synthesize PET data from the corresponding MRI acquisitions. Eventually we show an improved performance of the system when trained on the mixture of synthesized and real images
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Slimani, Amel. "Photonic approach for the study of dental hard tissues and carious lesion detection." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTT125.
Повний текст джерелаАнотація:
Les propriétés photoniques des tissus durs dentaires nous ont permis d’étudier l’email et la dentine a un niveau moléculaire (in vitro) en utilisant des techniques de microscopie optique non linéaires. La microscopie confocale Raman est technique d’imagine de haute résolution permettant d’analyse d’échantillon sans préparation spécifique ni marquage. Cette méthode nous a permis de reconstituer une cartographie de la réticulation du collagène et de la cristallinité au niveau de la jonction émail-dentine et cela avec une résolution spatiale non atteinte jusque-là. Cette analyse chimique de la jonction émail-dentine a permis de redéfinir la largeur de cette zone de transition. Cette largeur est nettement supérieure à celles proposées par les études précédentes. Par ailleurs, l’étude portant sur les changements de fluorescence intrinsèque entre les tissues dentaires sains et cariés suggèrent l’implication de la protoporphyrin IX et de la pentosidine dans l’expression de la fluorescence rouge des tissus cariés. La microscopie multiphotonique quant à elle nous a permis de détecter la lésion carieuse et de suivre son développement en utilisant la génération de seconde harmonique (SHG) et la fluorescence par excitation à deux photons (2PEF). Nos études ont démontré la validité du ratio SHG/2PEF comme paramètre fiable pour la détection de la lésion carieuse. Les études proposées par cette thèse montrent le potentiel des propriétés photoniques de l’émail et de la dentine en utilisant les microscopies Raman et multiphotoniques dans l’étude de ces tissus au niveau moléculaire. Cela offre de nouvelles perspectives en recherche et en applications cliniquesPhotonic properties of dental hard tissues allowed us to proceed to in vitro analysis of enamel and dentin on a molecular level. Confocal Raman microscopy has been used to produce a mapping of collagen cross-link and crystallinity of human dentin–enamel junction (DEJ) with a spatial resolution not achieved up to now. The method is a non-invasive, label-free and a high spatial resolution imaging technique. This chemical analysis of DEJ led us to redefine a wider width of this transition zone and advance our understanding of dental histology. A study on the intrinsic fluorescence changes of sound and carious tissues using conventional fluorescence microscopy suggests the involvement of protoporphyrin IX and pentosidine in the fluorescence red-shift observed in carious tissues. Multiphoton microscopy allowed to detect nonlinear optical signal changes during caries process using second harmonic generation (SHG) and two-photon excitation fluorescence (2PEF). Our studies led us to propose the ratio SHG/2PEF as valuable parameter to monitor caries lesion. Collectively, advances described in this thesis show the potential of photonic properties of enamel and dentin using Raman and multiphoton microcopies for molecular investigations on sound as much as on carious tissues. It opens new perspective in dental research and clinical applications
Книги з теми "Lesion detection"
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McNab, Claire. Fatal reunion: A Detective Inspector Carol Ashtonmystery. Tallahassee, FL: Naiad Press, 1989.
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King, Laurie R. The Art of Detection. New York: Random House Publishing Group, 2006.
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King, Laurie R. The art of detection. New York: Bantam Dell, 2006.
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The art of detection. Thorndike, Me: Center Point Pub., 2006.
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Pennypacker, Leslye Carol. Improved detection of human breast lesions following experimental training II: A medical student replication. [New Haven: s.n.], 1985.
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The Lesson of Her Death. New York: Random House Publishing Group, 2009.
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Deaver, Jeffery. The lesson of her death. New York: Bantam Books, 1994.
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The lesson of her death. London: Headline, 1993.
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Deaver, Jeffery. The lesson of her death. New York: Doubleday, 1993.
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The lesson of her death. London: Coronet Books, 1994.
Знайти повний текст джерелаЧастини книг з теми "Lesion detection"
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Neuhaus, K. W., R. Ellwood, A. Lussi, and N. B. Pitts. "Traditional Lesion Detection Aids." In Monographs in Oral Science, 42–51. Basel: KARGER, 2009. http://dx.doi.org/10.1159/000224211.
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Neuhaus, K. W., C. Longbottom, R. Ellwood, and A. Lussi. "Novel Lesion Detection Aids." In Monographs in Oral Science, 52–62. Basel: KARGER, 2009. http://dx.doi.org/10.1159/000224212.
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Işın, Ali, and Tazeen Sharif. "Deep Learning for Lung Lesion Detection." In 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018, 799–806. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04164-9_105.
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Li, Han, Hu Han, and S. Kevin Zhou. "Bounding Maps for Universal Lesion Detection." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 417–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59719-1_41.
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Manjaramkar, Arati, and Manesh Kokare. "Automated Red Lesion Detection: An Overview." In Advances in Intelligent Systems and Computing, 177–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1483-8_16.
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Longbottom, Christopher, and Bruce Vernon. "Bioluminescence Technology to Aid Lesion Activity Assessment." In Detection and Assessment of Dental Caries, 217–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16967-1_22.
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Li, He, Yutaro Iwamoto, Xianhua Han, Lanfen Lin, Hongjie Hu, and Yen-Wei Chen. "An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions." In Lecture Notes in Computer Science, 214–23. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16452-1_21.
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Marrone, Stefano, Gabriele Piantadosi, Roberta Fusco, Antonella Petrillo, Mario Sansone, and Carlo Sansone. "Automatic Lesion Detection in Breast DCE-MRI." In Image Analysis and Processing – ICIAP 2013, 359–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41184-7_37.
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Song, Yang, Weidong Cai, Heng Huang, Xiaogang Wang, Stefan Eberl, Michael Fulham, and Dagan Feng. "Similarity Guided Feature Labeling for Lesion Detection." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 284–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40811-3_36.
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Chen, Xiaoran, Nick Pawlowski, Ben Glocker, and Ender Konukoglu. "Unsupervised Lesion Detection with Locally Gaussian Approximation." In Machine Learning in Medical Imaging, 355–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_41.
Повний текст джерелаТези доповідей конференцій з теми "Lesion detection"
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Bai, Bing, Yongzhao Du, Ping Li, and Yuchun Lv. "Cervical Lesion Detection Net." In 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). IEEE, 2019. http://dx.doi.org/10.1109/icasid.2019.8925284.
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Dallali, Adel, Amel Slimen, Salim El Khediri, and Youssra Khemili. "Detection of lesion in mammograms." In 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET). IEEE, 2018. http://dx.doi.org/10.1109/aset.2018.8379902.
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Burgess, Arthur E., Francine L. Jacobson, and Philip F. Judy. "Lesion detection in digital mammograms." In Medical Imaging 2001, edited by Larry E. Antonuk and Martin J. Yaffe. SPIE, 2001. http://dx.doi.org/10.1117/12.430878.
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Reiazi, Reza, Reza Paydar, Ali Abbasian Ardakani, and Maryam Etedadialiabadi. "Mammography Lesion Detection Using Faster R-CNN Detector." In 7th International Conference on Natural Language Processing. Academy & Industry Research Collaboration Center (AIRCC), 2018. http://dx.doi.org/10.5121/csit.2018.80212.
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Chen, Kecheng, Kun Long, Yazhou Ren, Jiayu Sun, and Xiaorong Pu. "Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection." In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3475480.
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Luan, Bo, Yunxu Sun, Cheng Tong, Yuanxian Liu, and Hongshun Liu. "R-FCN Based Laryngeal Lesion Detection." In 2019 12th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2019. http://dx.doi.org/10.1109/iscid.2019.10112.
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Hatanaka, Yuji, Atsuki Mizukami, Chisako Muramatsu, Takeshi Hara, and Hiroshi Fujita. "Automated lesion detection in retinal images." In the 4th International Symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2093698.2093789.
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Djaouti, S. Mohammed, A. Koudache, A. Boudaïeb, Arno P. J. M. Siebes, Michael R. Berthold, Robert C. Glen, and Ad J. Feelders. "Polarimetric images segmentation for lesion detection." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793393.
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Sreena, S., and A. Lijiya. "Skin Lesion Analysis Towards Melanoma Detection." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993219.
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Rehman, Hafeez Ur, Syed Adnan Shah, Wakeel Ahmad, Syed Muhammad Anwar, and Nudrat Nida. "Deep retinanet for melanoma lesion detection." In 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2). IEEE, 2022. http://dx.doi.org/10.1109/icodt255437.2022.9787454.
Повний текст джерелаЗвіти організацій з теми "Lesion detection"
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Udupa, Jayaram K. A Novel Fuzzy Topological Approach to the Detection of Mammographic Lesion and Quantification of Parenchymal Density. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/adb241926.
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Lelievre, Sophie. Channeling Nanoparticles for Detection and Targeted Treatment of Breast Cancerous Lesions. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada555798.
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Lam, Stephen, John Yee, Wan L. Lam, Don Wilson, and Calum MacAulay. Blood-Based Biomarkers for Lung Cancer Early Detection and Evaluation of CT-Based Lesions. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada614404.
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Udupa, Jayaram K. A Novel Fuzzy Topological Approach to the Detection of Mammographic Lesions and Qualifications of Parenchymal Density. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada382475.
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Zhang, Chunxi, Fangfang Xie, Runchang Li, Ningxin Cui, and Jiayuan Sun. Robotic-assisted bronchoscopy for the diagnosis of peripheral pulmonary lesions: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0115.
Повний текст джерелаАнотація:
Review question / Objective: What is the overall diagnostic yield and complication rate of robotic-assisted bronchoscopy for peripheral pulmonary lesions? Condition being studied: Many of peripheral pulmonary lesions (PPLs) may represent early-stage lung cancer. Lung cancer is the leading cause of cancer mortality globally. Early diagnosis and treatment of lung cancer are crucial for a better prognosis. With the widespread use of low-dose computed tomography (LDCT), the detection rate of PPLs is increasing. As a result, the number of PPLs requiring biopsy is progressively increasing. Transbronchial lung biopsy (TBLB) and transthoracic needle aspiration (TTNA) are the main modalities of non-surgical biopsy for PPLs. TTNA has a diagnostic yield of 90%, however, it also has a pneumothorax rate of 25%. Since TBLB avoids destroying the structure of normal pleura and lung tissue, the incidence of complications is lower. Unfortunately, traditional flexible bronchoscopy has a modest sensitivity of 34% and 63% for lesions 2 cm, respectively. The advent of guided bronchoscopy has increased the diagnostic yield to 70%. However, there is still a gap in diagnostic yield compared with TTNA. The advent of robotic-assisted bronchoscopy (RAB) is expected to further improve the diagnostic yield of TBLB for PPLs. However, the diagnostic performance of RAB for PPLs has not reached a consensus.
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Edwards, Darrin C., Charles E. Metz, and Maryellen L. Giger. Investigation of Three-Group Classifiers to Fully Automate Detection and Classification of Breast Lesions in an Intelligent CAD Mammography Workstation. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada472082.
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Thomas Austin, Evan, Paul Kang, Chinedu Mmeje, Joseph Mashni, Mark Brenner, Phillip Koo, and John C Chang. Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices. Science Repository, December 2021. http://dx.doi.org/10.31487/j.aco.2021.02.02.
Повний текст джерелаАнотація:
Purpose: The purpose of this study was to validate the second version of the Prostate Imaging Reporting and Data System (PI-RADSv2) scores in predicting positive in-bore MRI-guided targeted prostate biopsy results across different non-university related institutions. The study focuses on PI-RADS v2 scoring because during the study period, PI-RADS v2.1 had not been released. Materials and Methods: This was a retrospective review of 147 patients who underwent multiparametric magnetic resonance imaging (mpMRI) of the pelvis followed by in-bore MRI-guided targeted prostate biopsy from December 2014 to May 2018. All lesions on mpMRI were rated according to PI-RADS v2 criteria. PI-RADS v2 scores were then compared to MR-guided biopsy results and pre-biopsy PSA values. Results: Prostate Cancer (PCa) was detected in 54% (80/147) of patients, with more prostate cancer being detected with each subsequent increase in PI-RADS scores. Specifically, biopsy results in patients with PI-RADS 3, 4, and 5 lesions resulted in PCa in 25.6% (10/39), 58.1% (33/55), and 86.0% (37/43) respectively. Clinically significant PCa (Gleason score ≥7) was detected in 17.9% (7/39), 52.7% (29/55), and 72% (31/43) of cases for PI-RADS 3, 4, and 5 lesions respectively. When the PI-RADS scoring and biopsy results were compared across different institutions, there was no difference in the PI-RADS scoring of lesions or in the positive biopsy rates of the lesions. The sensitivity, specificity, PPV, and NPV for PI-RADS 3-4 lesions were also not statistically different across the institutions for detecting Gleason 7 or greater lesions. Conclusion: Our results agree with prior studies that higher PI-RADS scores are associated with the presence of clinically significant PCa and suggest prostate lesions with PI-RADS scores 3-5 have sufficient evidence to warrant targeted biopsy. The comparison of PI-RADS score across different types of non-university practices revealed no difference in scoring and biopsy outcome, suggesting that PI-RADS v2 can be easily applied outside of the university medical center setting. Clinical Relevance: PI-RADS v2 can be applied homogeneously in the non-university setting without significant difference in outcome.
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Leonard, Talayna, Robert Lemme, Cati Kral, Briana Santiago, Chris Elberts, Stephanie Dewald, Patrick McGonagill, et al. High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis. Science Repository, December 2021. http://dx.doi.org/10.31487/j.aco.2021.02.03.
Повний текст джерелаАнотація:
Objective: Pancreatic ductal adenocarcinoma (PDAC) has the best survival when detected early with 5-year survival near 40% for small, resectable PDAC. We evaluate the undiagnosed PDAC imaging features on routine CT and their impact on resectability. Methods: 76 of the screened 134 CTs from 1/1/2012 to 12/31/2018 using our tumor registry were obtained prior to PDAC diagnosis for other indications at least one month before presentation. Each cross-sectional study was reviewed for features of early PDAC: pancreatic mass, pancreatic ductal dilatation, perivascular/peripancreatic soft-tissue infiltration, omental lesions/ascites, and lymphadenopathy. When such features were detectible by the reviewing radiologists, the original CT readings were classified as concordant/discrepant. Descriptive statistics are reported for discrepant reads, tumor resectability, and tumor size. Results: Of the 76 cases from 46 unique subjects (30 male/16 female), 25 CTs (33%) had undetected PDAC imaging features: masses (15/19 unreported), ductal dilatation (16/20 unreported), and peripancreatic/perivascular soft-tissue infiltration (20/36 unreported). 63% of early PDAC features were not identified initially. One year before clinical diagnosis, 75-80% of the PDAC cases were resectable; at < 6 months before clinical diagnosis, only 29% were resectable. Conclusion: Improving early detection of key PDAC features on routine CT examinations can potentially improve patient outcomes.
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Renaud, Alexander, Michael Forte, Nicholas Spore, Brittany Bruder, Katherine Brodie, Jessamin Straub, and Jeffrey Ruby. Evaluation of Unmanned Aircraft Systems for flood risk management : results of terrain and structure assessments. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45000.
Повний текст джерелаАнотація:
The 2017 Duck Unmanned Aircraft Systems (UAS) Pilot Experiment was conducted by the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory, Field Research Facility (FRF), to assess the potential for different UAS to support US Army Corps of Engineers coastal and flood risk management. By involving participants from multiple ERDC laboratories, federal agencies, academia, and private industry, the work unit leads were able to leverage assets, resources, and expertise to assess data from multiple UAS. This report compares datasets from several UAS to assess their potential to survey and observe coastal terrain and structures. In this report, UAS data product accuracy was analyzed within the context of three potential applications: (1) general coastal terrain survey accuracy across the FRF property; (2) small-scale feature detection and observation within the experiment infrastructure area; and (3) accuracy for surveying coastal foredunes. The report concludes by presenting tradeoffs between UAS accuracy and the cost to operate to aid in selection of the best UAS for a particular task. While the technology and exact UAS models vary through time, the lessons learned from this study illustrate that UAS are available at a variety of costs to satisfy varying coastal management data needs.
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Smit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, August 2022. http://dx.doi.org/10.57022/qpsm1481.
Повний текст джерелаАнотація:
Overview Skin cancer prevention is a component of the new Cancer Plan 2022–27, which guides the work of the Cancer Institute NSW. To lessen the impact of skin cancer on the community, the Cancer Institute NSW works closely with the NSW Skin Cancer Prevention Advisory Committee, comprising governmental and non-governmental organisation representatives, to develop and implement the NSW Skin Cancer Prevention Strategy. Primary Health Networks and primary care providers are seen as important stakeholders in this work. To guide improvements in skin cancer prevention and inform the development of the next NSW Skin Cancer Prevention Strategy, an up-to-date review of the evidence on the effectiveness and feasibility of skin cancer prevention activities in primary care is required. A research team led by the Daffodil Centre, a joint venture between the University of Sydney and Cancer Council NSW, was contracted to undertake an Evidence Check review to address the questions below. Evidence Check questions This Evidence Check aimed to address the following questions: Question 1: What skin cancer primary prevention activities can be effectively administered in primary care settings? As part of this, identify the key components of such messages, strategies, programs or initiatives that have been effectively implemented and their feasibility in the NSW/Australian context. Question 2: What are the main barriers and enablers for primary care providers in delivering skin cancer primary prevention activities within their setting? Summary of methods The research team conducted a detailed analysis of the published and grey literature, based on a comprehensive search. We developed the search strategy in consultation with a medical librarian at the University of Sydney and the Cancer Institute NSW team, and implemented it across the databases Embase, MEDLINE, PsycInfo, Scopus, Cochrane Central and CINAHL. Results were exported and uploaded to Covidence for screening and further selection. The search strategy was designed according to the SPIDER tool for Qualitative and Mixed-Methods Evidence Synthesis, which is a systematic strategy for searching qualitative and mixed-methods research studies. The SPIDER tool facilitates rigour in research by defining key elements of non-quantitative research questions. We included peer-reviewed and grey literature that included skin cancer primary prevention strategies/ interventions/ techniques/ programs within primary care settings, e.g. involving general practitioners and primary care nurses. The literature was limited to publications since 2014, and for studies or programs conducted in Australia, the UK, New Zealand, Canada, Ireland, Western Europe and Scandinavia. We also included relevant systematic reviews and evidence syntheses based on a range of international evidence where also relevant to the Australian context. To address Question 1, about the effectiveness of skin cancer prevention activities in primary care settings, we summarised findings from the Evidence Check according to different skin cancer prevention activities. To address Question 2, about the barriers and enablers of skin cancer prevention activities in primary care settings, we summarised findings according to the Consolidated Framework for Implementation Research (CFIR). The CFIR is a framework for identifying important implementation considerations for novel interventions in healthcare settings and provides a practical guide for systematically assessing potential barriers and facilitators in preparation for implementing a new activity or program. We assessed study quality using the National Health and Medical Research Council (NHMRC) levels of evidence. Key findings We identified 25 peer-reviewed journal articles that met the eligibility criteria and we included these in the Evidence Check. Eight of the studies were conducted in Australia, six in the UK, and the others elsewhere (mainly other European countries). In addition, the grey literature search identified four relevant guidelines, 12 education/training resources, two Cancer Care pathways, two position statements, three reports and five other resources that we included in the Evidence Check. Question 1 (related to effectiveness) We categorised the studies into different types of skin cancer prevention activities: behavioural counselling (n=3); risk assessment and delivering risk-tailored information (n=10); new technologies for early detection and accompanying prevention advice (n=4); and education and training programs for general practitioners (GPs) and primary care nurses regarding skin cancer prevention (n=3). There was good evidence that behavioural counselling interventions can result in a small improvement in sun protection behaviours among adults with fair skin types (defined as ivory or pale skin, light hair and eye colour, freckles, or those who sunburn easily), which would include the majority of Australians. It was found that clinicians play an important role in counselling patients about sun-protective behaviours, and recommended tailoring messages to the age and demographics of target groups (e.g. high-risk groups) to have maximal influence on behaviours. Several web-based melanoma risk prediction tools are now available in Australia, mainly designed for health professionals to identify patients’ risk of a new or subsequent primary melanoma and guide discussions with patients about primary prevention and early detection. Intervention studies have demonstrated that use of these melanoma risk prediction tools is feasible and acceptable to participants in primary care settings, and there is some evidence, including from Australian studies, that using these risk prediction tools to tailor primary prevention and early detection messages can improve sun-related behaviours. Some studies examined novel technologies, such as apps, to support early detection through skin examinations, including a very limited focus on the provision of preventive advice. These novel technologies are still largely in the research domain rather than recommended for routine use but provide a potential future opportunity to incorporate more primary prevention tailored advice. There are a number of online short courses available for primary healthcare professionals specifically focusing on skin cancer prevention. Most education and training programs for GPs and primary care nurses in the field of skin cancer focus on treatment and early detection, though some programs have specifically incorporated primary prevention education and training. A notable example is the Dermoscopy for Victorian General Practice Program, in which 93% of participating GPs reported that they had increased preventive information provided to high-risk patients and during skin examinations. Question 2 (related to barriers and enablers) Key enablers of performing skin cancer prevention activities in primary care settings included: • Easy access and availability of guidelines and point-of-care tools and resources • A fit with existing workflows and systems, so there is minimal disruption to flow of care • Easy-to-understand patient information • Using the waiting room for collection of risk assessment information on an electronic device such as an iPad/tablet where possible • Pairing with early detection activities • Sharing of successful programs across jurisdictions. Key barriers to performing skin cancer prevention activities in primary care settings included: • Unclear requirements and lack of confidence (self-efficacy) about prevention counselling • Limited availability of GP services especially in regional and remote areas • Competing demands, low priority, lack of time • Lack of incentives.