Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes
Quantitative Imaging in Medicine and Surgery , Volume 11 - Issue 1 p. 67- 83
Background: The objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke. Methods: Our pipeline was a two-stage cascade network that included a localization phase and a segmentation phase. The network framework was based on the 3D version of U-Net, but was refined in three ways: (I) by adding residual connections and a deep supervision strategy to cope with the vanishing problem in back-propagation; (II) by adopting dilated convolution in order to strengthen the capacity to capture contextual information; and (III) by establishing a hybrid objective function to address the extreme imbalance between foreground and background voxels. Results: We trained our networks on 15 cases and evaluated their performance based on 41 cases from the MICCAI Challenge 2009 dataset. A Dice similarity coefficient of 82.3% was achieved for the test cases. Conclusions: We developed a carotid segmentation method based on U-Net that can segment tiny carotid bifurcation lumens from very large backgrounds with no manual intervention. This was the first attempt to use deep learning to achieve carotid bifurcation segmentation in 3D CTA images. Our results indicate that deep learning is a promising method for automatically extracting carotid bifurcation lumens.
|, , , ,|
|Quantitative Imaging in Medicine and Surgery|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Zhou, T. (Tianshu), Tan, T. (Tao), Pan, X. (Xiaoyan), Tang, H. (Hui), & Li, J. (Jingsong). (2021). Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes. Quantitative Imaging in Medicine and Surgery, 11(1), 67–83. doi:10.21037/QIMS-20-286