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For the task of medical image segmentation, fully convolutional network (FCN)based architectures have been extensively used with various modifications. Arising trend in these architectures is to employ joint-learning of the targetregion with an auxiliary task, a method commonly known as multi-task learning.These approaches help impose smoothness and shape priors, which vanilla FCNapproaches do not necessarily incorporate. In this paper, we propose a novelplug-and-play module, which we term as Conv-MCD, which exploits structuralinformation in two ways - i) using the contour map and ii) using the distancemap, both of which can be obtained from ground truth segmentation maps with noadditional annotation costs. The key benefit of our module is the ease of itsaddition to any state-of-the-art architecture, resulting in a significantimprovement in performance with a minimal increase in parameters. Tosubstantiate the above claim, we conduct extensive experiments using 4state-of-the-art architectures across various evaluation metrics, and report asignificant increase in performance in relation to the base networks. Inaddition to the aforementioned experiments, we also perform ablative studiesand visualization of feature maps to further elucidate our approach.

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