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While computer-assisted planning methods are increasingly gaining importance, the necessary preparation of geometric data based on CT imaging remains largely a manual process. The aim of this work was to develop and test a method for the automated segmentation of the iliac crest for subsequent reconstruction planning. Methods: A total of 1,398 datasets with manual segmentations were obtained as ground truth, with a subset of 400 datasets used for training and validation of the Neural Networks and another subset of 177 datasets used solely for testing. A deep Convolutional Neural Network implemented in a 3D U-Net architecture using Tensorflow was employed to provide a pipeline for automatic segmentation. Transfer learning was applied for model training optimization. Evaluation metrics included the Dice Similarity Coefficient, Symmetrical Average Surface Distance, and a modified 95% Hausdorff Distance focusing on regions relevant for transplantation. Results: The automated segmentation achieved high accuracy, with qualitative and quantitative assessments demonstrating predictions closely aligned with ground truths. Quantitative evaluation of the correspondence yielded values for geometric agreement in the transplant-relevant area of 92% +\/- 7% (Dice coefficient) and average surface deviations of 0.605 +\/- 0.41\u00a0mm. In all cases, the bones were identified as contiguous objects in the correct spatial orientation. The geometries of the iliac crests were consistently and completely recognized on both sides without any gaps. Conclusions: The method was successfully used to extract the individual geometries of the iliac crest from CT data. Thus, it has the potential to serve as an essential starting point in a digitized planning process and to provide data for subsequent surgical planning. The complete automation of this step allows for efficient and reliable preparation of anatomical data for reconstructive surgeries.<\/jats:p>","DOI":"10.1038\/s41598-024-83031-0","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T09:29:17Z","timestamp":1736242157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning"],"prefix":"10.1038","volume":"15","author":[{"given":"Stefan","family":"Raith","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Pankert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f4natas","family":"de Souza Nascimento","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srikrishna","family":"Jaganathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Peters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Wien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"H\u00f6lzle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Modabber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"83031_CR1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.oraloncology.2009.11.006","volume":"46","author":"M Bak","year":"2010","unstructured":"Bak, M., Jacobson, A. 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