Residual Moment Loss for Medical Image Segmentation

TL;DR

This paper introduces a residual moment loss function that explicitly incorporates absolute location information into deep learning models, significantly improving medical image segmentation accuracy.

Contribution

The novel residual moment loss explicitly embeds absolute position information into segmentation training, enhancing the network's ability to locate and segment targets accurately.

Findings

RM loss improves segmentation accuracy on two datasets

Significant boost in model performance with RM loss

Effective in both 2D and 3D segmentation tasks

Abstract

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation. However, most existing methods encode the location information in an implicit way, e.g. the distance transform maps, which describe the relative distance from each pixel to the contour boundary, for the network to learn. These implicit approaches do not fully exploit the position information (i.e. absolute location) of targets. In this paper, we propose a novel loss function, namely residual moment (RM) loss, to explicitly embed the location information of segmentation targets during the training of deep learning networks. Particularly, motivated by image moments, the segmentation prediction map and ground-truth map are weighted by coordinate information. Then our RM loss encourages the networks to maintain the consistency between the two weighted maps, which promotes the segmentation networks to easily locate the targets and extract manifold-structure-related features. We validate the proposed RM loss by conducting extensive experiments on two publicly available datasets, i.e., 2D optic cup and disk segmentation and 3D left atrial segmentation. The experimental results demonstrate the effectiveness of our RM loss, which significantly boosts the accuracy of segmentation networks.