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

arXiv: https://arxiv.org/abs/1901.01238 · HTML: https://mlsanity.com/paper/1901.01238/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2103.04708/) (2021)
- [CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy](https://mlsanity.com/paper/2101.04266/) (2021)
- [A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation](https://mlsanity.com/paper/2109.07702/) (2021)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans](https://mlsanity.com/paper/2303.05126/) (2023)
- [Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2409.07793/) (2024)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2501.09116/) (2025)
- [A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation](https://mlsanity.com/paper/2108.07491/) (2021)
- [Brightearth roads: Towards fully automatic road network extraction from satellite imagery](https://mlsanity.com/paper/2406.14941/) (2024)

## Abstract (arXiv)

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±0.03 and 0.91±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±0.28 to 0.80±0.14.