Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

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 segmenta...

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Bibliographic Details
Main Authors: Reza Kalantar, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, Dow-Mu Koh
Format: article
Language:EN
Published: MDPI AG 2021
Subjects:
deep learning
pelvic cancer segmentation
radiology
radiation oncology
radiotherapy planning
Medicine (General)
R5-920
Online Access:https://doaj.org/article/7aff8e47b4f149db9b8f67a8f0fb6c4f
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https://doaj.org/article/7aff8e47b4f149db9b8f67a8f0fb6c4f

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