Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation

TL;DR

This paper introduces ES-UNet++, a novel liver and tumor segmentation model that enhances UNet++ with encoding feature supervision, leading to improved accuracy, faster convergence, and better pruning performance in medical image analysis.

Contribution

The paper proposes encoding feature supervised UNet++, a new method that improves UNet++ performance through encoding feature supervision, with significant accuracy and efficiency gains.

Findings

Achieved 95.6% dice score for liver segmentation.

Improved tumor segmentation dice score to 67.4%.

Supervision accelerates model convergence and enhances pruning efficiency.

Abstract

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.