Error Attention Interactive Segmentation of Medical Image Through Matting and Fusion

Abstract

Deep learning-based interactive segmentation has attracted research interest recently since it can smartly utilize user interactions to refine a coarse automatic segmentation to get higher accuracy for clinical use. Current methods usually transform user clicks to geodesic distance hint maps as guidance, then concatenate them with the raw image and coarse segmentation, and feed them into a refinement network. Such methods are insufficient in refining error region, which is a key capability required for interactive segmentation. In this paper, we propose Error Attention Interactive network with Matting and Fusion to auto-extract guide information of mis-segmentation region from two branches and transfer it into main segmentor. We first design Region Matting to obtain foreground and background mattings from coarse segmentation. And then we adopt the features extracted by two branches trained on above mattings as guidance. Attention-Fusion is further proposed to transfer the guidance to main segmentor effectively based on attention mechanism and feature concatenation. Experimental results on BraTS 2015 and our Neuroblastoma datasets have shown that our method significantly outperforms state-of-the-art methods, with the advantage of fewer interactions.