# A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation

## TL;DR

This paper introduces a multi-task learning regularization framework for cardiac MRI segmentation that improves accuracy and generalization by incorporating pixel-wise distance map regression into CNNs.

## Contribution

The authors propose a novel distance map regularizer added to CNNs for cardiac MRI segmentation, enhancing performance without increasing model complexity.

## Key findings

- Improved segmentation accuracy with average dice coefficients of 0.84 and 0.91.
- Enhanced cross-dataset generalization with up to 42% improvement in myocardium Dice coefficient.
- Regularizer improves robustness of CNNs for cardiac MRI segmentation.

## Abstract

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Several convolutional neural network (CNN) architectures have been proposed to segment the heart chambers from cardiac cine MR images. Here we propose a multi-task learning (MTL)-based regularization framework for cardiac MR image segmentation. The network is trained to perform the main task of semantic segmentation, along with a simultaneous, auxiliary task of pixel-wise distance map regression. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures on two publicly available cardiac cine MRI datasets, obtaining average dice coefficient of 0.84$\pm$0.03 and 0.91$\pm$0.04, respectively. Furthermore, we also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56$\pm$0.28 to 0.80$\pm$0.14.