ArticleRadiation oncology (London, England)2025
Unsupervised cycle-consistent network for 3D pelvic mono- or multimodal deformable image registration.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTo develop a novel deformable image registration method aimed at improving the accuracy of multimodal image registration in radiotherapy.
methodsAn unsupervised cycle-consistent network (CycleVOS) was proposed as the deformable image registration model, which integrates a cycle-consistent registration framework, a voxel optimization smooth (VOS) module, and a modality-independent neighbourhood descriptor (MIND) loss function. Two convolutional neural networks (CNNs) were employed to generate spatial deformable grids, ensuring consistency in the deformed images. In this study, the CycleVOS model was evaluated on multiple registration tasks, i.e., CT-CT registration, CT-CBCT registration and CT-MR registration tasks. The datasets consisted of 128 paired CT-MR images, 90 paired CT-CT images and 190 paired CT-CBCT images. Among these, 28 CT-MR, 25 CT-CT, and 40 CT-CBCT pairs were used as test data. The performance of the CycleVOS model was compared with that of the Elastix B-spline method using normalized mutual information (NMI) and the dice similarity coefficient (DSC) as evaluation metrics.
resultsIn terms of the test data, the NMI values obtained with the CycleVOS model and the B-spline method were 0.211 vs. 0.205 for CT-MR images, 0.508 vs. 0.485 for CT-CT images, and 0.404 vs. 0.379 for CT-CBCT images. The DSCs for the CycleVOS model were consistently greater than those for the B-spline method across all propagated contours in CT-CT data (bladder, bone marrow, body, CTV, and PTV) and CT-CBCT data (bladder, spinal cord, left femoral head, right femoral head, and bone marrow). Furthermore, compared with the B-spline method, the CycleVOS model trained on mixed data achieved comparable or superior accuracy.
conclusionsThe proposed CycleVOS model is highly accurate for both mono- and multimodal image registration. Its performance when trained on mixed data suggests that integrating various registration tasks into a single model is a feasible direction for future development. CLINICAL TRIAL NUMBER: Not applicable.
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