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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 | |
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 |