Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

Abstract Background We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with in...

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Bibliographic Details
Main Authors: Nina J. Wesdorp, J. Michiel Zeeuw, Sam C. J. Postma, Joran Roor, Jan Hein T. M. van Waesberghe, Janneke E. van den Bergh, Irene M. Nota, Shira Moos, Ruby Kemna, Fijoy Vadakkumpadan, Courtney Ambrozic, Susan van Dieren, Martinus J. van Amerongen, Thiery Chapelle, Marc R. W. Engelbrecht, Michael F. Gerhards, Dirk Grunhagen, Thomas M. van Gulik, John J. Hermans, Koert P. de Jong, Joost M. Klaase, Mike S. L. Liem, Krijn P. van Lienden, I. Quintus Molenaar, Gijs A. Patijn, Arjen M. Rijken, Theo M. Ruers, Cornelis Verhoef, Johannes H. W. de Wilt, Henk A. Marquering, Jaap Stoker, Rutger-Jan Swijnenburg, Cornelis J. A. Punt, Joost Huiskens, Geert Kazemier
Format: Article
Language:English
Published: SpringerOpen 2023-12-01
Series:European Radiology Experimental
Subjects:
Artificial intelligence
Deep learning
Colorectal cancer
Liver neoplasms
Tomography (x-ray computed)
Online Access:https://doi.org/10.1186/s41747-023-00383-4

Internet

https://doi.org/10.1186/s41747-023-00383-4

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