Voting-Based Contour-Aware Framework for Medical Image Segmentation

Accurate and automatic segmentation of medical images is in increasing demand for assisting disease diagnosis and surgical planning. Although Convolutional Neural Networks (CNNs) have shown great promise in medical image segmentation, they prefer to learn texture features over shape information. Moreover, recent studies have shown the promise that learning the data in a meaningful order can make the network perform better. Inspired by these points, we aimed to propose a two-stage medical image segmentation framework based on contour-aware CNN and voting strategy, which could consider the contour information and a meaningful learning order. In the first stage, we introduced a plug-and-play contour enhancement module that could be integrated into the encoder–decoder architecture to assist the model in learning boundary representations. In the second stage, we employed a voting strategy to update the model using easy samples in order to further increase the performance of our model. We conducted studies of the two publicly available CHAOS (MR) and hippocampus MRI datasets. The experimental results show that, when compared to the recent and popular existing models, the proposed framework can boost overall segmentation accuracy and achieve compelling performance, with dice coefficients of 91.2 ± 2.6% for the CHAOS dataset and 88.2 ± 0.4% for the hippocampus dataset.