Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions

Seo, Hyunseok; Bassenne, Maxime; Li, Xing

doi:10.1109/tmi.2020.3031913

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…While numerous CNN autosegmentation algorithms have been investigated for adult populations, the performance of these algorithms on pediatric populations has not been widely studied because of the lack of expertly labeled pediatric datasets. This dataset will allow the evaluation of existing autosegmentation approaches for pediatric populations and will also enable the application of new segmentation approaches 10 for the challenges of pediatric images. Pediatric autosegmentation algorithms will benefit numerous applications such as radiation therapy planning, 3,4 computer aided detection and diagnosis, 11 surgical planning, 12,13 and patient‐specific CT dose estimation 14–16 …”

Section: Discussionmentioning

confidence: 99%

Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours

et al. 2022

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Purpose Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest‐abdomen‐pelvis and abdomen‐pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. Acquisition and validation methods The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest‐abdomen‐pelvis or abdomen‐pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. Data format and usage notes The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric‐CT‐SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. Potential applications This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient‐specific organ dose estimation.

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Section: Discussionmentioning

confidence: 99%

Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours

et al. 2022

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“…We selected Adam [46] optimizer for the training procedure. To equally punish the underperformance in terms of false positives and false negatives, we used Dice loss function [47], [48]. The hyperparameters for every model were set as follows:…”

Section: F Implementation With Training and Test Detailsmentioning

confidence: 99%

LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

Sitnik¹,

Kopriva²

2022

Preprint

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Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task-or imaging modality specific architectures with a decisionmaking process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM) layer. Compared to the existing DN, the framework adds one hyperparameter and only modestly increase the number of learnable parameters. The method is aimed at, but not limited to, segmentation of lowdimensional medical images, such as color histopathological images of stained frozen sections. Since features in the LEFM layer are polynomial functions of the original features, proposed LEFM-Nets contribute to the interpretability of network decisions. In this work, we combined LEFM with the known networks: DeepLabv3+, UNet, UNet++ and MA-net. New LEFM-Nets are applied to the segmentation of adenocarcinoma of a colon in a liver from images of hematoxylin and eosin (H&E) stained frozen sections. LEFM-Nets are also tested on nuclei segmentation from images of H&E stained frozen sections of ten human organs. On the first problem, LEFM-Nets achieved statistically significant performance improvement in terms of micro balanced accuracy and F 1 score than original networks. When averaged over ten runs, LEFM-MA-net achieved balanced accuracy of 89.36% ± 1.28% compared to 88.02% ± 1.22% by the MA-net. Corresponding results for F 1 score are 84.96% ± 1.14% and 82.75% ± 1.10%. On the second problem, LEFM-Nets achieved only better performance in comparison with the original networks. LEFM-MA-net achieved balanced accuracy of 89.41% ± 0.29% compared to 89.30% ± 0.44% by the original MA-net. Results for F 1 score are 85.35% ± 0.25% and 85.12% ± 0.51%. The source code is available at https://github.com/dsitnik/lefm

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“…Deep learning convolutional neural networks (CNNs) for autosegmentation of anatomy have permeated into nearly every medical imaging discipline. [1][2][3][4][5] They have been used extensively for brain image analysis on MRI scans, [6][7][8][9] in digital pathology for nucleus and cell segmentation [10][11][12] , in ophthalmology for blood vessel and optic disc segmentation, 13,14 and in radiotherapy for segmentation of both organs at risk [15][16][17] and target volumes. 6,[18][19][20][21] Most recently, CNNs have been used to detect and contour COVID-19 symptoms on chest x-rays and CTs.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient‐specific CT dosimetry

et al. 2022

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This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation. Methods: A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-groupspecific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation. Results: Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. Conclusions: Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patientspecific CT dose estimation.

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Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions

Cited by 18 publications

References 19 publications

Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours

Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours

LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient‐specific CT dosimetry

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