Walter, A.; Hoegen-Saßmannshausen, P.; Stanic, G.; Rodrigues, J.P.; Adeberg, S.; Jäkel, O.; Frank, M.; Giske, K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers2024, 16, 415.
Walter, A.; Hoegen-Saßmannshausen, P.; Stanic, G.; Rodrigues, J.P.; Adeberg, S.; Jäkel, O.; Frank, M.; Giske, K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers 2024, 16, 415.
Walter, A.; Hoegen-Saßmannshausen, P.; Stanic, G.; Rodrigues, J.P.; Adeberg, S.; Jäkel, O.; Frank, M.; Giske, K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers2024, 16, 415.
Walter, A.; Hoegen-Saßmannshausen, P.; Stanic, G.; Rodrigues, J.P.; Adeberg, S.; Jäkel, O.; Frank, M.; Giske, K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers 2024, 16, 415.
Abstract
The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep learning methods depend heavily on consistent training data, thus state-of-the-art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on surrounding anatomical structures. Training strategies that directly following the construction rules given in the expert guidelines or the possibility to quantify the conformance of manually drawn contours with the guidelines are still missing. 71 anatomical structures that are relevant for CTV delineation in head and neck cancer patients according to the expert guidelines were segmented on 104 CT scans to assess the possibility to automate their segmentation by state-of-the-art deep learning methods. nnU-Net models were trained to automatically segment those 71 structures on planning CT scans. We report the DICE, HD and sDICE for 71 + 5 anatomical structures for most of which no previous segmentation accuracies have been reported. For those structures, for that prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2mm margin was larger than 80% for almost all structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.
Keywords
Automatic segmentation; anatomical structures; multi-label segmentation; clinical target volume delineation; lymph node level segmentation; expert guidelines; head and neck cancer.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
,
,
,
