A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making

Autor: Adrian C. Ruckli, Andreas K. Nanavati, Malin K. Meier, Till D. Lerch, Simon D. Steppacher, Sébastian Vuilleumier, Adam Boschung, Nicolas Vuillemin, Moritz Tannast, Klaus A. Siebenrock, Nicolas Gerber, Florian Schmaranzer

Publikováno v: Journal of Personalized Medicine; Volume 13; Issue 1; Pages: 153
Ruckli, Adrian C.; Nanavati, Andreas K.; Meier, Malin K.; Lerch, Till D.; Steppacher, Simon D.; Vuilleumier, Sébastian; Boschung, Adam; Vuillemin, Nicolas; Tannast, Moritz; Siebenrock, Klaus A.; Gerber, Nicolas; Schmaranzer, Florian (2023). A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making. Journal of personalized medicine, 13(1), p. 153. MDPI 10.3390/jpm13010153

(1) Background: To evaluate the performance of a deep learning model to automatically segment femoral head necrosis (FHN) based on a standard 2D MRI sequence compared to manual segmentations for 3D quantification of FHN. (2) Methods: Twenty-six patie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3070b8ce969368fe46ad094ba1709ad