Unsupervised Correspondence with Combined Geometric Learning and Imaging for Radiotherapy Applications

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation in 3D shapes was trained with head and neck organ segmentations from planning CT scans. We then extended the original model to incorporate imaging information using two approaches: 1) extracting features directly from image patches, and 2) including the mean square error between patches as part of the loss function. The correspondence and interpolation performance were evaluated using the geodesic error, chamfer distance and conformal distortion metrics, as well as distances between anatomical landmarks. Each of the models produced significantly better correspondences than the baseline non-rigid registration approach. The original model performed similarly to the model with direct inclusion of image features. The best performing model configuration incorporated imaging information as part of the loss function which produced more anatomically plausible correspondences. We will use the best performing model to identify corresponding anatomical points on organs to improve spatial normalisation, an important step in outcome modelling, or as an initialisation for anatomically informed registrations. All our code is publicly available at https://github.com/rrr-uom-projects/Unsup-RT-Corr-Net.
Original languageEnglish
Title of host publicationShape in Medical Imaging
Subtitle of host publicationInternational Workshop, ShapeMI 2023, Held in Conjunction with MICCAI 2023
EditorsChristian Wachinger, Beatriz Paniagua, Shireen Elhabian, Jianning Li, Jan Egger
Place of PublicationCham, Switzerland
PublisherSpringer Cham
Number of pages15
ISBN (Electronic)9783031469145
ISBN (Print)9783031469138
Publication statusPublished - 31 Oct 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • correspondence
  • un-supervised learning
  • geometric learning
  • image registration
  • radiotherapy

Research Beacons, Institutes and Platforms

  • Cancer
  • Manchester Cancer Research Centre


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