A Survey on Brain Tumor Segmentation Using MRI Data

International Journal of Research in Engineering, Science and Management

View Publication Info
 
 
Field Value
 
Title A Survey on Brain Tumor Segmentation Using MRI Data
 
Creator Sushanth, J. A.
Rudresh, G. S.
Prabhu, K. Anuj
Ali, Arif
 
Subject Artificial Intelligence
Brain tumor segmentation
Deep Learning
Image segmentation
MRI scan
Neural Networks
ResNet
 
Description Gliomas are the most frequent primary brain tumors, with varying degrees of aggressiveness, prognosis, and histological sub-regions, such as peritumoral edematous, necrotic core, active, and non-enhancing core. Variable intensity profiles spread throughout multi-parametric magnetic resonance imaging (mpMRI) images illustrate these sub-regions, representing diverse biological features. In longitudinal scans, the amount of resected tumor is also taken into account while evaluating the apparent tumor for possible progression diagnosis. Furthermore, there is growing evidence that accurate segmentation of multiple tumor sub-regions can provide a foundation for quantitative image analysis to predict patient overall survival. Manual segmentation of brain tumor regions is time-consuming and prone to human error, and its accuracy is determined by pathologists' experience. This study includes about 10 scientific papers that address a wide range of technical topics, including network architecture design, segmentation under imbalanced situations, and multi-modality processes. We use this survey to present a complete assessment of newly established deep learning-based brain tumor segmentation algorithms, taking into account the astonishing breakthroughs produced by state-of-the-art technology.
 
Publisher RESAIM
 
Date 2022-05-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://www.journals.resaim.com/ijresm/article/view/1982
 
Source International Journal of Research in Engineering, Science and Management; Vol. 5 No. 4 (2022); 169-171
2581-5792
 
Language eng
 
Relation https://www.journals.resaim.com/ijresm/article/view/1982/1922
 
Rights Copyright (c) 2022 J. A. Sushanth, G. S. Rudresh, K. Anuj Prabhu, Arif Ali
https://creativecommons.org/licenses/by/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library