用距离图回归提升小病灶分割，兼顾形状完整性和精度
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation
- 将距离图作为新标签，通过轻量回归网络预测
- 在LiTS和临床数据集上显著优于现有方法
- 适合小目标、形状不规则的医学图像分割场景
小目标分割（如肿瘤分割）是医学图像分析中的关键挑战。尽管深度学习方法表现优异，但传统方法仅依赖二值分割掩码。受二值掩码与距离图间严格映射关系启发，本文采用距离图作为新型监督信号，并设计一个轻量级回归网络（LR-Net），将距离图计算转化为回归任务，充分挖掘距离图中的丰富信息。同时，提出一种基于距离图的形状感知损失，利用距离图作为惩罚图以恢复物体完整形状。在MICCAI 2017肝脏肿瘤分割（LiTS）挑战数据集及临床数据集上的实验表明，该方法在小目标分割上优于基于分类的方法及其他现有先进方法。
原文摘要 · Abstract (English)
Small object segmentation, like tumor segmentation, is a difficult and critical task in the field of medical image analysis. Although deep learning based methods have achieved promising performance, they are restricted to the use of binary segmentation mask. Inspired by the rigorous mapping between binary segmentation mask and distance map, we adopt distance map as a novel ground truth and employ a network to fulfill the computation of distance map. Specially, we propose a new segmentation framework that incorporates the existing binary segmentation network and a light weight regression network (dubbed as LR-Net). Thus, the LR-Net can convert the distance map computation into a regression task and leverage the rich information of distance maps. Additionally, we derive a shape-aware loss by employing distance maps as penalty map to infer the complete shape of an object. We evaluated our approach on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset and a clinical dataset. Experimental results show that our approach outperforms the classification-based methods as well as other existing state-of-the-arts.
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