TY - JOUR AU - Xue, Yuan AU - Tang, Hui AU - Qiao, Zhi AU - Gong, Guanzhong AU - Yin, Yong AU - Qian, Zhen AU - Huang, Chao AU - Fan, Wei AU - Huang, Xiaolei PY - 2020/04/03 Y2 - 2024/03/28 TI - Shape-Aware Organ Segmentation by Predicting Signed Distance Maps JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.6946 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6946 SP - 12565-12572 AB - <p>In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since there is a rigorous mapping between the Signed Distance Map (SDM) calculated from object boundary contours and the binary segmentation map, we exploit the feasibility of learning the SDM directly from medical scans. By converting the segmentation task into predicting an SDM, we show that our proposed method retains superior segmentation performance and has better smoothness and continuity in shape. To leverage the complementary information in traditional segmentation training, we introduce an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. We validate our proposed models by conducting extensive experiments on a hippocampus segmentation dataset and the public MICCAI 2015 Head and Neck Auto Segmentation Challenge dataset with multiple organs. While our carefully designed backbone 3D segmentation network improves the Dice coefficient by more than 5% compared to current state-of-the-arts, the proposed model with SDM learning produces smoother segmentation results with smaller Hausdorff distance and average surface distance, thus proving the effectiveness of our method.</p> ER -