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Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms
Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925103/ https://www.ncbi.nlm.nih.gov/pubmed/29849536 http://dx.doi.org/10.1155/2018/4303161 |
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author | Liao, Chun-Chih Chen, Ya-Fang Xiao, Furen |
author_facet | Liao, Chun-Chih Chen, Ya-Fang Xiao, Furen |
author_sort | Liao, Chun-Chih |
collection | PubMed |
description | Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided. |
format | Online Article Text |
id | pubmed-5925103 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-59251032018-05-30 Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms Liao, Chun-Chih Chen, Ya-Fang Xiao, Furen Int J Biomed Imaging Review Article Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided. Hindawi 2018-04-12 /pmc/articles/PMC5925103/ /pubmed/29849536 http://dx.doi.org/10.1155/2018/4303161 Text en Copyright © 2018 Chun-Chih Liao et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Liao, Chun-Chih Chen, Ya-Fang Xiao, Furen Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title | Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title_full | Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title_fullStr | Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title_full_unstemmed | Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title_short | Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms |
title_sort | brain midline shift measurement and its automation: a review of techniques and algorithms |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925103/ https://www.ncbi.nlm.nih.gov/pubmed/29849536 http://dx.doi.org/10.1155/2018/4303161 |
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