Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, while the second stage is an edge enhanced network (E$^2$Net) for more accurate liver and tumor segmentation. E$^2$Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network. We also propose a deep cross feature fusion module to refine multi-scale features from both objects and their edges. E$^2$Net is more easily and efficiently trained with a small labeled dataset, and it can be trained/tested on the original 2D CT slices (resolve resampling error issue in 3D models). The proposed framework has shown superior performance on both liver and liver tumor segmentation compared to several state-of-the-art 2D, 3D and 2D/3D hybrid frameworks.

我们提出了一种两阶段的2D肝脏和肝瘤分割框架，第一阶段是粗略的肝脏分割网络，第二阶段是用于更精确的肝脏和肿瘤分割的边缘增强网络(E$^2$Net)。E$^2$Net在网络内明确地建模互补对象（肝脏和肿瘤）及其边缘信息，以保留器官和病变边界。我们还提出了一个深度交叉特征融合模块，以从两个对象及其边缘中细化多尺度特征。建议的框架在肝和肝肿瘤分割方面表现出优越性能。

E$^2$Net：一种在CT扫描中用于精确肝脏和肿瘤分割的边缘增强网络