A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images

Semantic image segmentation is a popular image segmentation technique where each pixel in an image is labeled with an object class. This technique has become a vital part of image analysis nowadays as it facilitates the description, categorization, and visualization of the regions of interest in an...

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Bibliographic Details
Published in:SN computer science 2021-09, Vol.2 (5), p.397, Article 397
Main Authors: Kar, Mithun Kumar, Nath, Malaya Kumar, Neog, Debanga Raj
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Artificial neural networks
Automation
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computer vision
Data Structures and Information Theory
Datasets
Deep learning
Image analysis
Image segmentation
Information Systems and Communication Service
Machine learning
Medical imaging
Neural networks
Open source software
Pattern Recognition and Graphics
Review Article
Semantic segmentation
Semantics
Software Engineering/Programming and Operating Systems
Vision
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Summary:Semantic image segmentation is a popular image segmentation technique where each pixel in an image is labeled with an object class. This technique has become a vital part of image analysis nowadays as it facilitates the description, categorization, and visualization of the regions of interest in an image. The recent developments in computer vision algorithms and the increasing availability of large datasets have made semantic image segmentation very popular in the field of computer vision. Motivated by the human visual system which can identify objects in a complex scene very efficiently, researchers are interested in building a model that can semantically segment an image into meaningful object classes. This paper reviews deep learning-based semantic segmentation techniques that use deep neural network architectures for image segmentation of biomedical images. We have provided a discussion on the fundamental concepts related to deep learning methods used in semantic segmentation for the benefit of readers. The standard datasets and existing deep network architectures used in both medical and non-medical fields are discussed with their significance. Finally, this paper concludes by discussing the challenges and future research directions in the field of deep learning-based semantic segmentation for applications in the medical field.
ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-021-00784-5