Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation [article]

Minyoung Chung, Jingyu Lee, Minkyung Lee, Jeongjin Lee, Yeong-Gil Shin
<span title="2019-10-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with
more &raquo; ... global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1808.00739v5</a> <a target="_blank" rel="external noopener" href="">fatcat:24r7mzcbsfdz7njb2jttsgkewm</a> </span>
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