# Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation

## TL;DR

This paper introduces Conv-MCD, a versatile plug-and-play module that enhances medical image segmentation by leveraging contour and distance maps, improving existing architectures with minimal added complexity.

## Contribution

The novel Conv-MCD module exploits structural information from ground truth maps, easily integrates into existing models, and significantly boosts segmentation performance.

## Key findings

- Significant performance improvements across four architectures.
- Effective use of contour and distance maps without extra annotation.
- Minimal increase in model parameters.

## Abstract

For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. A rising trend in these architectures is to employ joint-learning of the target region with an auxiliary task, a method commonly known as multi-task learning. These approaches help impose smoothness and shape priors, which vanilla FCN approaches do not necessarily incorporate. In this paper, we propose a novel plug-and-play module, which we term as Conv-MCD, which exploits structural information in two ways - i) using the contour map and ii) using the distance map, both of which can be obtained from ground truth segmentation maps with no additional annotation costs. The key benefit of our module is the ease of its addition to any state-of-the-art architecture, resulting in a significant improvement in performance with a minimal increase in parameters. To substantiate the above claim, we conduct extensive experiments using 4 state-of-the-art architectures across various evaluation metrics, and report a significant increase in performance in relation to the base networks. In addition to the aforementioned experiments, we also perform ablative studies and visualization of feature maps to further elucidate our approach.