Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images.


Journal

IEEE journal of biomedical and health informatics
ISSN: 2168-2208
Titre abrégé: IEEE J Biomed Health Inform
Pays: United States
ID NLM: 101604520

Informations de publication

Date de publication:
09 2020
Historique:
pubmed: 15 2 2020
medline: 25 9 2021
entrez: 15 2 2020
Statut: ppublish

Résumé

Extranodal natural killer/T cell lymphoma (ENKL), nasal type is a kind of rare disease with a low survival rate that primarily affects Asian and South American populations. Segmentation of ENKL lesions is crucial for clinical decision support and treatment planning. This paper is the first study on computer-aided diagnosis systems for the ENKL segmentation problem. We propose an automatic, coarse-to-fine approach for ENKL segmentation using adversarial networks. In the coarse stage, we extract the region of interest bounding the lesions utilizing a segmentation neural network. In the fine stage, we use an adversarial segmentation network and further introduce a multi-scale L

Identifiants

pubmed: 32054593
doi: 10.1109/JBHI.2020.2972694
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2599-2608

Auteurs

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Classifications MeSH