Enhancing Foreground Boundaries for Medical Image Segmentation
This addresses a specific issue in medical image segmentation for clinical applications, but it is incremental as it builds on existing loss functions.
The paper tackles the problem of poor segmentation at boundary areas in medical images due to fuzzy appearance contrast, proposing a boundary enhancement loss that improves or matches state-of-the-art segmentation accuracy.
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning. Given the various modalities of medical images, the automated or semi-automated segmentation approaches have been used to identify and parse organs, bones, tumors, and other regions-of-interest (ROI). However, these contemporary segmentation approaches tend to fail to predict the boundary areas of ROI, because of the fuzzy appearance contrast caused during the imaging procedure. To further improve the segmentation quality of boundary areas, we propose a boundary enhancement loss to enforce additional constraints on optimizing machine learning models. The proposed loss function is light-weighted and easy to implement without any pre- or post-processing. Our experimental results validate that our loss function are better than, or at least comparable to, other state-of-the-art loss functions in terms of segmentation accuracy.