Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics.

Details

Serval ID
serval:BIB_93C1E02D6980
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics.
Journal
Seminars in musculoskeletal radiology
Author(s)
Bach Cuadra M., Favre J., Omoumi P.
ISSN
1098-898X (Electronic)
ISSN-L
1089-7860
Publication state
Published
Issued date
02/2020
Peer-reviewed
Oui
Volume
24
Number
1
Pages
50-64
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.
Pubmed
Web of science
Create date
29/01/2020 15:14
Last modification date
19/02/2020 7:19
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