Open AccessAutomated Detection, Segmentation, and Classification of Pleural Effusion From Computed Tomography Scans Using Machine Learning
Author(s) -
Raphael Sexauer,
Shan Yang,
Thomas Weikert,
Julien Poletti,
Jens Bremerich,
Jan A Roth,
Alexander Sauter,
Constantin Anastasopoulos
Publication year - 2022
Publication title -
investigative radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 114
eISSN - 1536-0210
pISSN - 0020-9996
DOI - 10.1097/rli.0000000000000869
Subject(s) - medicine , sørensen–dice coefficient , segmentation , receiver operating characteristic , pleural effusion , radiology , random forest , artificial intelligence , computed tomography , nuclear medicine , image segmentation , computer science
This study trained and evaluated algorithms to detect, segment, and classify simple and complex pleural effusions on computed tomography (CT) scans.