# Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

arXiv: https://arxiv.org/abs/2008.08946 · HTML: https://mlsanity.com/paper/2008.08946/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion](https://mlsanity.com/paper/2202.02000/) (2022)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting](https://mlsanity.com/paper/2504.05966/) (2025)
- [Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline](https://mlsanity.com/paper/2103.10892/) (2021)
- [Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration](https://mlsanity.com/paper/2310.09221/) (2023)

## Abstract (arXiv)

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation.