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

翻译：本研究旨在开发一种模型，用于准确识别不同患者器官分割之间的对应点，以服务于放射治疗应用。我们利用计划CT扫描中的头颈部器官分割数据，训练了一个可同步进行三维形状对应与插值估计的模型。随后通过两种方式将原始模型扩展以融入成像信息：1）直接从图像块中提取特征；2）将图像块间的均方误差纳入损失函数。对应与插值性能通过测地线误差、倒角距离、共形畸变指标以及解剖标志点距离进行评估。所有模型产生的对应结果均显著优于基于非刚性配准的基线方法。原始模型与直接嵌入图像特征的模型性能相当。性能最优的模型配置将成像信息融入损失函数，可生成更具解剖学合理性的对应关系。我们将采用该最优模型识别器官上的解剖对应点，以改进空间归一化（该步对结果建模至关重要），或作为解剖引导配准的初始化依据。所有代码已开源发布于：https://github.com/rrr-uom-projects/Unsup-RT-Corr-Net