TY - JOUR

T1 - Contour-aware multi-label chest X-ray organ segmentation

AU - Kholiavchenko, M.

AU - Sirazitdinov, I.

AU - Kubrak, K.

AU - Badrutdinova, R.

AU - Kuleev, R.

AU - Yuan, Y.

AU - Vrtovec, T.

AU - Ibragimov, B.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Chest X-ray (CXR) images

KW - Convolutional neural networks

KW - Deep learning architectures

KW - Image segmentation

KW - JSRT database

UR - http://www.scopus.com/inward/record.url?scp=85079447147&partnerID=8YFLogxK

U2 - 10.1007/s11548-019-02115-9

DO - 10.1007/s11548-019-02115-9

M3 - Journal article

C2 - 32034633

AN - SCOPUS:85079447147

VL - 15

SP - 425

EP - 436

JO - International journal of computer assisted radiology and surgery

JF - International journal of computer assisted radiology and surgery

SN - 1861-6410

IS - 3

ER -