Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation

Abstract

In current state-of-the-art medical image segmentation methods, boundary details are typically enhanced by employing complex structures, which impose an additional computational burden for inference and cannot be embedded in the latest segmentation architectures as general-purpose boundary enhancers. This paper proposes a simple and flexible method, namely, pixel-wise triplet learning, for effectively improving boundary discrimination without imposing an additional computational burden. The method uses pixel-level triplet loss to enable segmentation models to learn more discriminative feature representations at boundaries, and it can be easily incorporated into the latest segmentation networks as a generic boundary booster and used for binary and multiclass medical segmentation tasks. Extensive experiments on nine medical datasets that cover five mainstream medical imaging modalities showed that the method is simple yet effective in achieving accurate boundary discrimination and high segmentation performance.