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

arXiv: https://arxiv.org/abs/1812.04914 · HTML: https://mlsanity.com/paper/1812.04914/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease](https://mlsanity.com/paper/2101.10799/) (2021)
- [Binary segmentation of medical images using implicit spline representations and deep learning](https://mlsanity.com/paper/2102.12759/) (2021)
- [3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors](https://mlsanity.com/paper/2212.04579/) (2022)
- [Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI](https://mlsanity.com/paper/2102.01822/) (2021)
- [Multimodal Information Interaction for Medical Image Segmentation](https://mlsanity.com/paper/2404.16371/) (2024)
- [DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification](https://mlsanity.com/paper/2202.13304/) (2022)
- [Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network](https://mlsanity.com/paper/2208.04940/) (2022)
- [Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation](https://mlsanity.com/paper/2410.10551/) (2024)
- [μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation](https://mlsanity.com/paper/2406.16724/) (2024)

## Abstract (arXiv)

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.