Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.
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Authors
Kalantar, R
Lin, G
Winfield, JM
Messiou, C
Lalondrelle, S
Blackledge, MD
Koh, D-M
Lin, G
Winfield, JM
Messiou, C
Lalondrelle, S
Blackledge, MD
Koh, D-M
Document Type
Journal Article
Date
2021-10-22
Date Accepted
2021-10-19
Abstract
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.
Citation
Diagnostics (Basel, Switzerland), 2021, 11 (11)
Source Title
Publisher
MDPI
ISSN
2075-4418
eISSN
2075-4418
2075-4418
2075-4418
Collections
Research Team
Computational Imaging
Gynaecological Cancer
Gynaecological Cancer