Academic literature on the topic 'SEGMENTATION SYSTEM'

Purpose: In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. Methods: In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets. Results: In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results. Conclusions: We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.

Crowdsourced demarcations of object boundaries in images (segmentations) are important for many vision-based applications. A commonly reported challenge is that a large percentage of crowd results are discarded due to concerns about quality. We conducted three studies to examine (1) how does the quality of crowdsourced segmentations differ for familiar everyday images versus unfamiliar biomedical images?, (2) how does making familiar images less recognizable (rotating images upside down) influence crowd work with respect to the quality of results, segmentation time, and segmentation detail?, and (3) how does crowd workers’ judgments of the ambiguity of the segmentation task, collected by voting, differ for familiar everyday images and unfamiliar biomedical images? We analyzed a total of 2,525 segmentations collected from 121 crowd workers and 1,850 votes from 55 crowd workers. Our results illustrate the potential benefit of explicitly accounting for human familiarity with the data when designing computer interfaces for human interaction.

Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. This study is concerned with the segmentation of dancing motion captured by accelerometry and its possible applications, such as pattern learning and recognition, or gestural control of devices. To that effect, an automatic segmentation system was formulated and tested. Two participants were asked to ‘dance with one arm’ while their motion was measured by an accelerometer. The performances were recorded on video, and manually segmented by six annotators later. The annotations were used to optimize the automatic segmentation system, maximizing a novel similarity score between computed and annotated segmentations. The computed segmentations with highest similarity to each annotation were then manually assessed by the annotators, resulting in Precision between 0.71 and 0.89, and Recall between 0.82 to 1.

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.

Abstract Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.

Background Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive disclosure. Blockchain (including Bitcoin and Ethernet Square) is an effective technology to address the problems of traditional drug traceability systems. However, some risks impact the reliability of blockchain, such as information explosion, sensitive information leakage, and poor scalability. Objective To avoid the risks associated with the application of blockchain, we propose a lightweight block chain framework. Methods In this framework, both horizontal and vertical segmentations are performed when designing the blocks, and effective strategies are provided for both segmentations. For horizontal segmentation operations, the header and body of the blockchain are separated and stored in the blockchain, and the body is stored in the InterPlanetary File System. For vertical segmentation operations, the blockchain is cut off according to time or size. For the addition of new blocks, miners only need to copy the latest part of the blockchain and append the tail and vertical segmentation of the block through the consensus mechanism. Results Our framework could greatly reduce the size of the blockchain and improve the verification efficiency. Conclusions Experimental results have shown that the efficiency improves compared with ethernet when a new block is added to the blockchain and a search is conducted.

In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of the cooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01.