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

Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)
    165 Downloads (Pure)


    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.

    Original languageEnglish
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Publication statusPublished - 22 Aug 2017


    • annotation strategies
    • Biomedical imaging
    • continuous max-flow
    • Image segmentation
    • Labeling
    • Manuals
    • markov random field
    • multi-atlas segmentation
    • partial annotations
    • Robustness
    • Sociology
    • Training
    • unifying framework


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