Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
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DOI：
10.1609/aaai.v34i07.6946
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Yuan Xue;Hui Tang;Zhi Qiao;G. Gong;Yong Yin;Zhen Qian;Chao Huang;Wei Fan;Xiaolei Huang
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文献类型：
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作者：
Yuan Xue;Hui Tang;Zhi Qiao;G. Gong;Yong Yin;Zhen Qian;Chao Huang;Wei Fan;Xiaolei Huang
在这项工作中，我们建议解决目前基于深度学习的器官分割系统中存在的问题，即它们产生的结果往往不能捕捉到目标器官的整体形状，而且往往缺乏平稳性。由于从目标边界轮廓计算的符号距离图(SDM)与二值分割图之间存在严格的映射，因此我们探索了直接从医学扫描中学习SDM的可行性。通过将分割任务转化为对SDM的预测，我们的方法保持了优越的分割性能，并且在形状上具有更好的光滑性和连续性。为了充分利用传统分割训练中的互补信息，我们引入了一种近似的Heaviside函数，通过同时预测SDMS和分割图来训练模型。我们通过在海马体分割数据集和公共MICCAI2015头颈部多器官自动分割挑战数据集上进行广泛的实验来验证我们提出的模型。虽然我们精心设计的主干3D分割网络将Dice系数提高了5%以上，但带有SDM学习的模型以更小的Hausdorff距离和平均表面距离产生了更平滑的分割结果，从而证明了我们方法的有效性。
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.