Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer.

cervical cancer deep learning tumor segmentation

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
11 May 2022
Historique:
received: 25 03 2022
revised: 02 05 2022
accepted: 05 05 2022
entrez: 28 5 2022
pubmed: 29 5 2022
medline: 29 5 2022
Statut: epublish

Résumé

Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (

Identifiants

pubmed: 35625977
pii: cancers14102372
doi: 10.3390/cancers14102372
pmc: PMC9139985
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : The Western Norway Regional Health Authority
ID : 461120
Organisme : Norwegian Research Council
ID : 461136
Organisme : Trond Mohn Foundation
ID : BFS2018TMT06
Organisme : Trond Mohn Foundation
ID : BFS2016TMT01

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Auteurs

Erlend Hodneland (E)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Mathematics, University of Bergen, 5020 Bergen, Norway.

Satheshkumar Kaliyugarasan (S)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063 Bergen, Norway.

Kari Strøno Wagner-Larsen (KS)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Section of Radiology, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.

Njål Lura (N)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Section of Radiology, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.

Erling Andersen (E)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Clinical Engineering, Haukeland University Hospital, 5021 Bergen, Norway.

Hauke Bartsch (H)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Informatics, University of Bergen, 5020 Bergen, Norway.

Noeska Smit (N)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Informatics, University of Bergen, 5020 Bergen, Norway.

Mari Kyllesø Halle (MK)

Department of Obstetrics and Gynecology, Haukeland University Hospital, 5053 Bergen, Norway.
Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway.

Camilla Krakstad (C)

Department of Obstetrics and Gynecology, Haukeland University Hospital, 5053 Bergen, Norway.
Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway.

Alexander Selvikvåg Lundervold (AS)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063 Bergen, Norway.

Ingfrid Salvesen Haldorsen (IS)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5009 Bergen, Norway.
Section of Radiology, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.

Classifications MeSH