Contour-aware multi-label chest X-ray organ segmentation
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Contour-aware multi-label chest X-ray organ segmentation. / Kholiavchenko, M.; Sirazitdinov, I.; Kubrak, K.; Badrutdinova, R.; Kuleev, R.; Yuan, Y.; Vrtovec, T.; Ibragimov, B.
In: International Journal of Computer Assisted Radiology and Surgery, Vol. 15, No. 3, 2020, p. 425-436.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 244279695