  Liver tumor segmentation in CT images is a critical step in the diagnosis,
surgical planning and postoperative evaluation of liver disease. An automatic
liver and tumor segmentation method can greatly relieve physicians of the heavy
workload of examining CT images and better improve the accuracy of diagnosis.
In the last few decades, many modifications based on U-Net model have been
proposed in the literature. However, there are relatively few improvements for
the advanced UNet++ model. In our paper, we propose an encoding feature
supervised UNet++(ES-UNet++) and apply it to the liver and tumor segmentation.
ES-UNet++ consists of an encoding UNet++ and a segmentation UNet++. The
well-trained encoding UNet++ can extract the encoding features of label map
which are used to additionally supervise the segmentation UNet++. By adding
supervision to the each encoder of segmentation UNet++, U-Nets of different
depths that constitute UNet++ outperform the original version by average 5.7%
in dice score and the overall dice score is thus improved by 2.1%. ES-UNet++ is
evaluated with dataset LiTS, achieving 95.6% for liver segmentation and 67.4%
for tumor segmentation in dice score. In this paper, we also concluded some
valuable properties of ES-UNet++ by conducting comparative anaylsis between
ES-UNet++ and UNet++:(1) encoding feature supervision can accelerate the
convergence of the model.(2) encoding feature supervision enhances the effect
of model pruning by achieving huge speedup while providing pruned models with
fairly good performance.
