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

TL;DR

CFUN is a novel heart segmentation method that combines Faster R-CNN and U-net for efficient, accurate, and fast whole heart segmentation with a new edge-based loss function.

Contribution

The paper introduces CFUN, a combined Faster R-CNN and U-net pipeline with a new 3D Edge-loss for improved efficiency and accuracy in heart segmentation.

Findings

Achieves competitive segmentation performance

Reduces inference time significantly

Utilizes a novel edge-based auxiliary loss

Abstract

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.