论文标题
E $^2 $ NET：边缘增强网络，用于CT扫描的准确肝脏和肿瘤分割
E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
论文作者
论文摘要
通过CT扫描开发有效的肝脏和肝肿瘤分割模型对于肝癌诊断，手术计划和癌症治疗的成功非常重要。在这项工作中，我们提出了一个两阶段的框架，用于2D肝脏和肿瘤分割。第一阶段是粗肝分割网络，而第二阶段是边缘增强网络（E $^2 $ net），以进行更准确的肝脏和肿瘤分割。 E $^2 $明确模型互补对象（肝脏和肿瘤）及其在网络中的边缘信息，以保留器官和病变边界。我们在E $^2 $ NET中引入了一个边缘预测模块，并设计了肝脏和肿瘤边界之间的边缘距离图，该图被用作训练边缘增强网络的额外监督信号。我们还提出了一个深层特征融合模块，以优化对象及其边缘的多尺度特征。 E $^2 $ NET更容易有效地使用标记的小数据集对其进行培训，并且可以在原始的2D CT切片上进行训练/测试（在3D模型中解析重新采样错误问题）。与几个最新的2D，3D和2D/3D混合框架相比，提出的框架在肝脏和肝肿瘤分割方面表现出了出色的性能。
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.
