# Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images

arXiv: https://arxiv.org/abs/1709.09641 · HTML: https://mlsanity.com/paper/1709.09641/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation](https://mlsanity.com/paper/2102.02033/) (2021)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion](https://mlsanity.com/paper/2202.02000/) (2022)
- [One registration is worth two segmentations](https://mlsanity.com/paper/2405.10879/) (2024)
- [Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline](https://mlsanity.com/paper/2103.10892/) (2021)
- [Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation](https://mlsanity.com/paper/2204.11090/) (2022)

## Abstract (arXiv)

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing.