Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

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...

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Bibliographic Details
Main Authors: Erlend Hodneland, Satheshkumar Kaliyugarasan, Kari Strøno Wagner-Larsen, Njål Lura, Erling Andersen, Hauke Bartsch, Noeska Smit, Mari Kyllesø Halle, Camilla Krakstad, Alexander Selvikvåg Lundervold, Ingfrid Salvesen Haldorsen
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Cancers
Subjects:
cervical cancer
deep learning
tumor segmentation
Online Access:https://www.mdpi.com/2072-6694/14/10/2372

Internet

https://www.mdpi.com/2072-6694/14/10/2372

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