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In this paper, we propose a novel heart segmentation pipeline CombiningFaster R-CNN and U-net Network (CFUN). Due to Faster R-CNN's preciselocalization ability and U-net's powerful segmentation ability, CFUN needs onlyone-step detection and segmentation inference to get the whole heartsegmentation result, obtaining good results with significantly reducedcomputational cost. Besides, CFUN adopts a new loss function based on edgeinformation named 3D Edge-loss as an auxiliary loss to accelerate theconvergence of training and improve the segmentation results. Extensiveexperiments on the public dataset show that CFUN exhibits competitivesegmentation performance in a sharply reduced inference time. Our source codeand the model are publicly available at https://github.com/Wuziyi616/CFUN.

CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation