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The process of medical 3D-printing involves several steps, each of which might be prone to its own set of errors. The segmentation error (SegE), the digital editing error (DEE) and the printing error (PrE) are the most important partial errors. Approaches to evaluate these have not yet been implemented in a joint concept. Consequently, information on the stability of the overall process is often lacking and possible process optimizations are difficult to implement. In this study, SegE, DEE, and PrE are evaluated individually, and error propagation is used to examine the cumulative effect of the partial errors.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The partial errors were analyzed employing surface deviation analyses. The effects of slice thickness, kernel, threshold, software and printers were investigated. The total error was calculated as the sum of SegE, DEE and PrE.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The higher the threshold value was chosen, the smaller were the segmentation results. The deviation values varied more when the CT slices were thicker and when the threshold was more distant from a value of around -400\u00a0HU. Bone kernel-based segmentations were prone to artifact formation. The relative reduction in STL file size [as a proy for model complexity] was greater for higher levels of smoothing and thinner slice thickness of the DICOM datasets. The slice thickness had a minor effect on the surface deviation caused by smoothing, but it was affected by the level of smoothing. The PrE was mainly influenced by the adhesion of the printed part to the build plate. Based on the experiments, the total error was calculated for an optimal and a worst-case parameter configuration. Deviations of 0.0093\u00a0mm\u2009\u00b1\u20090.2265\u00a0mm and 0.3494\u00a0mm\u2009\u00b1\u20090.8001\u00a0mm were calculated for the total error.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Various parameters affecting geometric deviations in medical 3D-printing were analyzed. Especially, soft reconstruction kernels seem to be advantageous for segmentation. The concept of error propagation can contribute to a better understanding of the process specific errors and enable future analytical approaches to calculate the total error based on process parameters.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s41205-024-00242-x","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T04:30:03Z","timestamp":1732249803000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis"],"prefix":"10.1186","volume":"10","author":[{"given":"Lukas","family":"Juergensen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Rischen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Hasselmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Toennemann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arne","family":"Pollmanns","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Gosheger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Schulze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"242_CR1","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3390\/jpm11060517","volume":"11","author":"M Schulze","year":"2021","unstructured":"Schulze M, Gosheger D, Bockholt S, de Vaal M, Budny T, T\u00f6nnemann M, P\u00fctzler J, B\u00f6vingloh AS, Rischen R, Hofbauer V, L\u00fcbben T, Deventer N, Ahrens H. 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