scholarly journals Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

2021 ◽  
Vol 12 ◽  
Author(s):  
Augustin C. Ogier ◽  
Marc-Adrien Hostin ◽  
Marc-Emmanuel Bellemare ◽  
David Bendahan
Keyword(s):  
Image Segmentation ◽  
Neuromuscular Disorders ◽  
Neuromuscular Diseases ◽  
Therapeutic Strategies ◽  
Quantitative Mri ◽  
Slow Progression ◽  
Recent Emergence ◽  
Magnetic Resonance Imaging Mri ◽  
Muscle Segmentation ◽  
Mr Image Segmentation

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases

2020 ◽  
Author(s):  
Harmen Reyngoudt ◽  
Benjamin Marty ◽  
Jean-Marc Boisserie ◽  
Julien Le Louër ◽  
Cedi Koumako ◽  
...  
Keyword(s):  
Neuromuscular Diseases ◽  
Quantitative Mri ◽  
Individual Muscle ◽  
Fat Fraction ◽  
Muscle Segmentation

scholarly journals Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

2016 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Justinas Jucevicius ◽  
Povilas Treigys ◽  
Jolita Bernataviciene ◽  
Ruta Briediene ◽  
Ieva Naruševiciute ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Mr Image ◽  
Prostate Segmentation ◽  
Image Appearance ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Image Segmentation

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

scholarly journals Longer T2relaxation time is a marker of hypothalamic gliosis in mice with diet-induced obesity

2013 ◽  
Vol 304 (11) ◽  
pp. E1245-E1250 ◽  
Author(s):  
Donghoon Lee ◽  
Joshua P. Thaler ◽  
Kathryn E. Berkseth ◽  
Susan J. Melhorn ◽  
Michael W. Schwartz ◽  
...  
Keyword(s):  
Relaxation Time ◽  
High Field ◽  
Relaxation Times ◽  
Quantitative Mri ◽  
Human Studies ◽  
Mri Findings ◽  
Diet Induced Obesity ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
Mr Brain

A hallmark of brain injury from infection, vascular, neurodegenerative, and other disorders is the development of gliosis, which can be detected by magnetic resonance imaging (MRI). In rodent models of diet-induced obesity (DIO), high-fat diet (HFD) consumption rapidly induces inflammation and gliosis in energy-regulating regions of the mediobasal hypothalamus (MBH), and recently we reported MRI findings suggestive of MBH gliosis in obese humans. Thus, noninvasive imaging may obviate the need to assess MBH gliosis using histopathological end points, an obvious limitation to human studies. To investigate whether quantitative MRI is a valid tool with which to measure MBH gliosis, we performed analyses, including measurement of T2relaxation time from high-field MR brain imaging of mice fed HFD and chow-fed controls. Mean bilateral T2relaxation time was prolonged significantly in the MBH, but not in the thalamus or cortex, of HFD-fed mice compared with chow-fed controls. Histological analysis confirmed evidence of increased astrocytosis and microglial accumulation in the MBH of HFD-fed mice compared with controls, and T2relaxation times in the right MBH correlated positively with mean intensity of glial fibrillary acidic protein staining (a marker of astrocytes) in HFD-fed animals. Our findings indicate that T2relaxation time obtained from high-field MRI is a useful noninvasive measurement of HFD-induced gliosis in the mouse hypothalamus with potential for translation to human studies.

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