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| Home > Publications database > Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| Home > Publications database > Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy |
| Journal Article | DKFZ-2025-00353 |
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2025
Taylor & Francis
Abingdon
Please use a persistent id in citations: doi:10.1080/21681163.2025.2455752
Abstract: Knowing about anatomical deformations in patient images is crucial for adaptive image-guided radiation therapy. Biomechanical models ensure biofidelity in deformable image registration, but manual contouring limits their clinical use. We investigate the application of automatically generated contours for a biomechanical registration model in head and neck cancer treatment. For that, we automatically generate individual bone segmentations on planning CT scans examining a custom-trained nnU-Net model and the ready-trained TotalSegmentator model. Both sets of segmentations are evaluated using DICE, Hausdorff Distance and surface DICE. We investigate their impact on the build-up of the biomechanical articulated skeleton model by deviations in joint positioning and CT-CT registration accuracy using target registration error (TRE). The custom-trained model achieves 1.51 ± 0.26 mm TRE, with no significant difference in registration accuracy. While the TotalSegmentator does not provide all structures needed for the complete biomechanical model build-up. Overall, deep learning–based automatic bone segmentation can replace manual contouring in this model, matching its performance.
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