Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.
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Date
2021-10-22Author
Kalantar, R
Lin, G
Winfield, JM
Messiou, C
Lalondrelle, S
Blackledge, MD
Koh, D-M
Type
Journal Article
Metadata
Show full item recordAbstract
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
Collections
Research team
Computational Imaging
Gynaecological Cancer
Language
eng
Date accepted
2021-10-19
License start date
2021-10-22
Citation
Diagnostics (Basel, Switzerland), 2021, 11 (11)
Publisher
MDPI