Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications

TL;DR

This study introduces an unsupervised model combining geometric learning and imaging data to accurately identify corresponding points in 3D organ shapes for radiotherapy, outperforming traditional registration methods.

Contribution

The paper presents a novel unsupervised approach that integrates imaging information into geometric correspondence models for improved accuracy in medical shape analysis.

Findings

The model outperforms baseline non-rigid registration methods.

Incorporating imaging as a loss function component yields more anatomically plausible correspondences.

The approach is effective for organ shape correspondence in radiotherapy planning.

Abstract

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