Deep learning approaches to biomedical image segmentation

The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identification, and quantification of patterns in the medical images. T...

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Bibliographic Details
Published in:Informatics in medicine unlocked Vol. 18; p. 100297
Main Authors: Rizwan I Haque, Intisar, Neubert, Jeremiah
Format: Journal Article
Language:English
Published: Elsevier Ltd 2020
Elsevier
Subjects:
Biomedical images
Deep learning
Image segmentation
Machine learning
Deep learning
Image segmentation
Biomedical images
Machine learning
ISSN:2352-9148, 2352-9148
Online Access:Get full text
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Summary:The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identification, and quantification of patterns in the medical images. The pivotal point of these advancements is the essential capability of the deep learning approaches to obtain hierarchical feature representations directly from the images, which in turn is eliminating the need for handcrafted features. Deep learning is expeditiously turning into the state-of-the-art for medical image processing and has resulted in performance improvements in diverse clinical applications. In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications. Finally, some research issues are highlighted and the future need for further improvements is pointed out.
ISSN:2352-9148
2352-9148
DOI:10.1016/j.imu.2020.100297