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
this https URL

为了精确地识别不同患者器官分割之间的相应点以应用于放射治疗，本研究旨在开发一个模型。通过使用头颈部器官分割的 CT 扫描训练了一个在 3D 形状中同时进行对应和插值估计的模型。通过两种方法扩展了原始模型，其中一种是直接从图像补丁中提取特征，另一种是将补丁之间的均方误差作为损失函数的一部分。使用地形误差、卡迈尔距离、共形畸变度量以及解剖标志点之间的距离评估了对应和插值性能。每个模型相较于基线的非刚性配准方法都产生了更好的对应结果，原始模型与直接包含图像特征的模型表现相似。最佳性能的模型配置将图像信息作为损失函数的一部分，产生了更符合解剖学的对应结果。我们将使用最佳性能的模型来识别器官上相应的解剖点，以改进空间归一化，这是结果建模的重要步骤，或作为解剖信息注册的初始化。我们的所有代码都公开在此 https URL。