dc.contributor.author |
Kholiavchenko M. |
|
dc.contributor.author |
Sirazitdinov I. |
|
dc.contributor.author |
Kubrak K. |
|
dc.contributor.author |
Badrutdinova R. |
|
dc.contributor.author |
Kuleev R. |
|
dc.contributor.author |
Yuan Y. |
|
dc.contributor.author |
Vrtovec T. |
|
dc.contributor.author |
Ibragimov B. |
|
dc.date.accessioned |
2021-02-25T20:47:07Z |
|
dc.date.available |
2021-02-25T20:47:07Z |
|
dc.date.issued |
2020 |
|
dc.identifier.issn |
1861-6410 |
|
dc.identifier.uri |
https://dspace.kpfu.ru/xmlui/handle/net/162401 |
|
dc.description.abstract |
© 2020, CARS. Purpose: Segmentation of organs from chest X-ray images is an essential task for an accurate and reliable diagnosis of lung diseases and chest organ morphometry. In this study, we investigated the benefits of augmenting state-of-the-art deep convolutional neural networks (CNNs) for image segmentation with organ contour information and evaluated the performance of such augmentation on segmentation of lung fields, heart, and clavicles from chest X-ray images. Methods: Three state-of-the-art CNNs were augmented, namely the UNet and LinkNet architecture with the ResNeXt feature extraction backbone, and the Tiramisu architecture with the DenseNet. All CNN architectures were trained on ground-truth segmentation masks and additionally on the corresponding contours. The contribution of such contour-based augmentation was evaluated against the contour-free architectures, and 20 existing algorithms for lung field segmentation. Results: The proposed contour-aware segmentation improved the segmentation performance, and when compared against existing algorithms on the same publicly available database of 247 chest X-ray images, the UNet architecture with the ResNeXt50 encoder combined with the contour-aware approach resulted in the best overall segmentation performance, achieving a Jaccard overlap coefficient of 0.971, 0.933, and 0.903 for the lung fields, heart, and clavicles, respectively. Conclusion: In this study, we proposed to augment CNN architectures for CXR segmentation with organ contour information and were able to significantly improve segmentation accuracy and outperform all existing solution using a public chest X-ray database. |
|
dc.relation.ispartofseries |
International Journal of Computer Assisted Radiology and Surgery |
|
dc.subject |
Chest X-ray (CXR) images |
|
dc.subject |
Convolutional neural networks |
|
dc.subject |
Deep learning architectures |
|
dc.subject |
Image segmentation |
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dc.subject |
JSRT database |
|
dc.title |
Contour-aware multi-label chest X-ray organ segmentation |
|
dc.type |
Article |
|
dc.relation.ispartofseries-issue |
3 |
|
dc.relation.ispartofseries-volume |
15 |
|
dc.collection |
Публикации сотрудников КФУ |
|
dc.relation.startpage |
425 |
|
dc.source.id |
SCOPUS18616410-2020-15-3-SID85079447147 |
|