CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation
This addresses efficient medical image segmentation for healthcare applications, but it is incremental as it builds on existing methods.
The paper tackles whole heart segmentation by combining Faster R-CNN and U-Net into a one-step pipeline, achieving competitive performance with sharply reduced inference time.
In this paper, we propose a novel heart segmentation pipeline Combining Faster R-CNN and U-net Network (CFUN). Due to Faster R-CNN's precise localization ability and U-net's powerful segmentation ability, CFUN needs only one-step detection and segmentation inference to get the whole heart segmentation result, obtaining good results with significantly reduced computational cost. Besides, CFUN adopts a new loss function based on edge information named 3D Edge-loss as an auxiliary loss to accelerate the convergence of training and improve the segmentation results. Extensive experiments on the public dataset show that CFUN exhibits competitive segmentation performance in a sharply reduced inference time. Our source code and the model are publicly available at https://github.com/Wuziyi616/CFUN.