Magnetic Resonance Imaging of the Lung: Automated Segmentation Methods

Background The pathology of Parkinson’s disease leads to morphological changes in brain structure. Currently, the progressive changes in gray matter volume that occur with time and are specific to patients with Parkinson’s disease, compared to healthy controls, remain unclear. High-tesla magnetic resonance imaging might be useful in differentiating neurological disorders by brain cortical changes. Purpose We aimed to investigate patterns in gray matter changes in patients with Parkinson’s disease by using an automated segmentation method with 7-tesla magnetic resonance imaging. Material and Methods High-resolution T1-weighted 7 tesla magnetic resonance imaging volumes of 24 hemispheres were acquired from 12 Parkinson’s disease patients and 12 age- and sex-matched healthy controls with median ages of 64.5 (range, 41–82) years and 60.5 (range, 25–74) years, respectively. Subgroup analysis was performed according to whether axial motor symptoms were present in the Parkinson’s disease patients. Cortical volume, cortical thickness, and subcortical volume were measured using a high-resolution image processing technique based on the Desikan-Killiany-Tourville atlas and an automated segmentation method (FreeSurfer version 6.0). Results After cortical reconstruction, in 7 tesla magnetic resonance imaging volume segmental analysis, compared with the healthy controls, the Parkinson’s disease patients showed global cortical atrophy, mostly in the prefrontal area (rostral middle frontal, superior frontal, inferior parietal lobule, medial orbitofrontal, rostral anterior cingulate area), and subcortical volume atrophy in limbic/paralimbic areas (fusiform, hippocampus, amygdala). Conclusion We first demonstrated that 7 tesla magnetic resonance imaging detects structural abnormalities in Parkinson’s disease patients compared to healthy controls using an automated segmentation method. Compared with the healthy controls, the Parkinson’s disease patients showed global prefrontal cortical atrophy and hippocampal area atrophy.

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Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Lecture Notes in Computer Science - Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge ◽

10.1007/978-3-030-93722-5_37 ◽

2022 ◽

pp. 344-351

Author(s):

Kumaradevan Punithakumar ◽

Adam Carscadden ◽

Michelle Noga

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Right Ventricle ◽

Convolutional Neural Networks ◽

Automated Segmentation ◽

Resonance Imaging ◽

Deep Convolutional Neural Networks ◽

The Right

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Visual analysis of automated segmentation in the diagnosis of focal cortical dysplasias with magnetic resonance imaging

Epilepsy & Behavior ◽

10.1016/j.yebeh.2019.106684 ◽

2020 ◽

Vol 102 ◽

pp. 106684

Author(s):

M. Magdalena Sepúlveda ◽

Gonzalo M. Rojas ◽

Evelyng Faure ◽

Claudio R. Pardo ◽

Facundo las Heras ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Visual Analysis ◽

Automated Segmentation ◽

Resonance Imaging ◽

Cortical Dysplasias

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Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

PLoS ONE ◽

10.1371/journal.pone.0135715 ◽

2015 ◽

Vol 10 (8) ◽

pp. e0135715 ◽

Cited By ~ 8

Author(s):

Jessica Lebenberg ◽

Alain Lalande ◽

Patrick Clarysse ◽

Irene Buvat ◽

Christopher Casta ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Cardiovascular Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Function ◽

Cardiovascular Magnetic Resonance Imaging ◽

Automated Segmentation ◽

Resonance Imaging

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Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.2.03 ◽

2021 ◽

Vol 29 (2) ◽

Author(s):

Abang Mohd Arif Anaqi Abang Isa ◽

Kuryati Kipli ◽

Ahmad Tirmizi Jobli ◽

Muhammad Hamdi Mahmood ◽

Siti Kudnie Sahari ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Ischemic Stroke ◽

Magnetic Resonance ◽

Acute Ischemic Stroke ◽

Clustering Algorithm ◽

Brain Mri ◽

Segmentation Algorithm ◽

Automated Segmentation ◽

Resonance Imaging ◽

Greyscale Image

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

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