Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis

Autor: Lukas Juergensen, Robert Rischen, Julian Hasselmann, Max Toennemann, Arne Pollmanns, Georg Gosheger, Martin Schulze

Publikováno v: 3D Printing in Medicine, Vol 10, Iss 1, Pp 1-21 (2024)

Abstract Background The use of 3D-printing in medicine requires a context-specific quality assurance program to ensure patient safety. The process of medical 3D-printing involves several steps, each of which might be prone to its own set of errors. T

Externí odkaz: https://doaj.org/article/047cce8f60054bb4b439089be6b31f77

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Segmentation Errors in the Measurement of Volumetric Parameters by Swept-Source Anterior Segment Optical Coherence Tomography

Autor: Hailiu Chen, Jie Meng, Peng Lu, Dan Ye, Yunxuan Li, Lu Cheng, Yangyunhui Li, Xiaoling Liang, Wenyong Huang, Jingjing Huang

Publikováno v: Frontiers in Medicine, Vol 8 (2021)

Purpose: To investigate the error rate of segmentation in the automatic measurement of anterior chamber volume (ACV) and iris volume (IV) by swept-source anterior segment optical coherence tomography (SS-ASOCT) in narrow-angle and wide-angle eyes.Met

Externí odkaz: https://doaj.org/article/224dc125370c4925918170cc999c928f

Automatic Identification of Segmentation Errors for Radiotherapy Using Geometric Learning

Autor: Henderson, Edward G. A., Green, Andrew F., van Herk, Marcel, Vasquez Osorio, Eliana M., Wang, Linwei, Dou, Qi, Fletcher, P. Thomas, Speidel, Stefanie, Li, Shuo

Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022-25th International Conference, Proceedings, 13435 LNCS, 319-329
Lecture Notes in Computer Science ISBN: 9783031164422

Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::649d872f8431b1a8427ef395c6a63c55
http://www.scopus.com/inward/record.url?scp=85139064626&partnerID=8YFLogxK

Segmentation error and macular thickness measurements obtained with spectral-domain optical coherence tomography devices in neovascular age-related macular degeneration

Autor: Moosang Kim, Seung Jun Lee, Jisang Han, Seung-Young Yu, Hyung Woo Kwak

Publikováno v: Indian Journal of Ophthalmology, Vol 61, Iss 5, Pp 213-217 (2013)

Purpose: To evaluate frequency and severity of segmentation errors of two spectral-domain optical coherence tomography (SD-OCT) devices and error effect on central macular thickness (CMT) measurements. Materials and Methods: Twenty-seven eyes of 25 p

Externí odkaz: https://doaj.org/article/0fda6cf2d0384345a8b670af51bbbbfd