  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.
