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pro vyhledávání: '"Segeroth, Martin"'
Autor:
D'Antonoli, Tugba Akinci, Berger, Lucas K., Indrakanti, Ashraya K., Vishwanathan, Nathan, Weiß, Jakob, Jung, Matthias, Berkarda, Zeynep, Rau, Alexander, Reisert, Marco, Küstner, Thomas, Walter, Alexandra, Merkle, Elmar M., Segeroth, Martin, Cyriac, Joshy, Yang, Shan, Wasserthal, Jakob
Purpose: To develop an open-source and easy-to-use segmentation model that can automatically and robustly segment most major anatomical structures in MR images independently of the MR sequence. Materials and Methods: In this study we extended the cap
Externí odkaz:
http://arxiv.org/abs/2405.19492
Autor:
Wasserthal, Jakob, Breit, Hanns-Christian, Meyer, Manfred T., Pradella, Maurice, Hinck, Daniel, Sauter, Alexander W., Heye, Tobias, Boll, Daniel, Cyriac, Joshy, Yang, Shan, Bach, Michael, Segeroth, Martin
Publikováno v:
Radiol Artif Intell 2023;5(5):e230024
We present a deep learning segmentation model that can automatically and robustly segment all major anatomical structures in body CT images. In this retrospective study, 1204 CT examinations (from the years 2012, 2016, and 2020) were used to segment
Externí odkaz:
http://arxiv.org/abs/2208.05868