# Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

arXiv: https://arxiv.org/abs/1605.00029 · HTML: https://mlsanity.com/paper/1605.00029/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04882/) (2023)
- [W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2305.18624/) (2023)
- [Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning](https://mlsanity.com/paper/2502.05282/) (2025)
- [Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation](https://mlsanity.com/paper/2412.19026/) (2024)
- [PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation](https://mlsanity.com/paper/2602.11628/) (2026)

## Abstract (arXiv)

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.