Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Autor: Alain Farron, Fabio Becce, Stacey Gidoin, Oskar Truffer, Elham Taghizadeh, Alexandre Terrier, Sylvain Eminian, Philippe Büchler

Publikováno v: European radiology, vol. 31, no. 1, pp. 181-190
European Radiology
Taghizadeh, Elham; Truffer, Oskar; Becce, Fabio; Eminian, Sylvain; Gidoin, Stacey; Terrier, Alexandre; Farron, Alain; Büchler, Philippe (2021). Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets. European radiology, 31(1), pp. 181-190. Springer-Verlag 10.1007/s00330-020-07070-7

Objectives This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltrat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0f97c9c646870e3a8927e3b4efba0ba
https://serval.unil.ch/notice/serval:BIB_CF8206F40600

Evaluating an Architecture Conformance Monitoring Solution

Autor: Oscar Nierstrasz, Kirill Levitin, Mircea Lungu, Andrea Caracciolo, Oskar Truffer

Publikováno v: IWESEP@SANER
2016 7th International Workshop on Empirical Software Engineering in Practice (IWESEP), 41-44
STARTPAGE=41;ENDPAGE=44;TITLE=2016 7th International Workshop on Empirical Software Engineering in Practice (IWESEP)

Architectural rules are often defined but rarely tested. Current tools offer limited functionality and often require significant effort to be configured, automated and integrated within existing platforms. We propose a platform that is aimed at reduc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a288fe6d9aff9f9bc080c02701e7eaad
https://doi.org/10.1109/iwesep.2016.12