# VoteNet++: Registration Refinement for Multi-Atlas Segmentation

arXiv: https://arxiv.org/abs/2010.13484 · HTML: https://mlsanity.com/paper/2010.13484/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment](https://mlsanity.com/paper/2202.03563/) (2022)
- [CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection](https://mlsanity.com/paper/2502.14891/) (2025)

## Abstract (arXiv)

Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the knee.