Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.
deep learning
pelvic cancer segmentation
radiation oncology
radiology
radiotherapy planning
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
22 Oct 2021
22 Oct 2021
Historique:
received:
09
08
2021
revised:
14
10
2021
accepted:
19
10
2021
entrez:
27
11
2021
pubmed:
28
11
2021
medline:
28
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34829310
pii: diagnostics11111964
doi: 10.3390/diagnostics11111964
pmc: PMC8625809
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
ID : N/A
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