Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines

Iordanescu, Gheorghe; Venkatasubramanian, Palamadai N.; Wyrwicz, Alice M.

doi:10.1002/mrm.23138

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…This method can be used in addition to histological evaluations as a useful tool for the development of new therapies. Future improvement of the method may be based on the automatic segmentation of the amyloid load to speed-up plaque quantification after Gd-staning ( Iordanescu et al, 2012 ). Also, our study showed that individual plaque labeling is feasible in vivo with a conventional MR contrast agent, as long as the contrast agent remains furtive for the BBB.…”

Section: Discussionmentioning

confidence: 99%

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy

Santin

Vandenberghe

Herard

et al. 2016

Front. Aging Neurosci.

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Extracellular deposition of β amyloid plaques is an early event associated to Alzheimer’s disease. Here, we have used in vivo gadolinium-stained high resolution (29∗29∗117 μm3) magnetic resonance imaging (MRI) to follow-up in a longitudinal way individual amyloid plaques in APP/PS1 mice and evaluate the efficacy of a new immunotherapy (SAR255952) directed against protofibrillar and fibrillary forms of Aβ. APP/PS1 mice were treated for 5 months between the age of 3.5 and 8.5 months. SAR255952 reduced amyloid load in 8.5-months-old animals, but not in 5.5-months animals compared to mice treated with a control antibody (DM4). Histological evaluation confirmed the reduction of amyloid load and revealed a lower density of amyloid plaques in 8.5-months SAR255952-treated animals. The longitudinal follow-up of individual amyloid plaques by MRI revealed that plaques that were visible at 5.5 months were still visible at 8.5 months in both SAR255952 and DM4-treated mice. This suggests that the amyloid load reduction induced by SAR255952 is related to a slowing down in the formation of new plaques rather than to the clearance of already formed plaques.

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Section: Discussionmentioning

confidence: 99%

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy

Santin

Vandenberghe

Herard

et al. 2016

Front. Aging Neurosci.

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“…Teipel et al () proposed an alternative voxel‐based analysis of T2 relaxation time in AβPP/PS1 mice as a surrogate measure of plaque burden. In a recent study, Further automatic plaque segmentation algorithm have been reported (Iordanescu et al, ) which can reliably quantify amyloid plaques in mouse brains by the combination of watershed transform and SVM. This method can detect the spatial and temporal progression of amyloid deposition, which is necessary for the understanding of the Aβ formation, progression of plaque pathology in mouse models of AD and accessing the efficacy of amyloid clearance therapies.…”

Section: Amyloid Plaque Imaging Using Mrmmentioning

confidence: 99%

Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease

Lin

et al. 2015

Microscopy Res & Technique

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Over the past two decades, various Alzheimer's disease (AD) trangenetic mice models harboring genes with mutation known to cause familial AD have been created. Today, high-resolution magnetic resonance microscopy (MRM) technology is being widely used in the study of AD mouse models. It has greatly facilitated and advanced our knowledge of AD. In this review, most of the attention is paid to fundamental of MRM, the construction of standard mouse MRM brain template and atlas, the detection of amyloid plaques, following up on brain atrophy and the future applications of MRM in transgenic AD mice. It is believed that future testing of potential drugs in mouse models with MRM will greatly improve the predictability of drug effect in preclinical trials.

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“…In recent years, successful attempts with an implementation of the watershed algorithm was made on samples of different anatomical structures evaluated with a use of MR. In this field of studies, an evaluation of the bone erosions of the hand [11,12], brain amyloid plaques [13] and even breast lesions [14] were presented. There are a few publications present image processing algorithms dedicated to the knee joint, but directed to evaluate cartilage [15] on weight bearing parts.…”

Section: Introductionmentioning

confidence: 99%

Image processing in detection of knee joints injuries based on MRI images

Kohut¹,

Holak²,

Obuchowicz³

2017

J. vibroeng.

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This paper presents image processing methods for visualization and classification of medial meniscus tears. The first method uses watershed with a threshold segmentation approach. The algorithm was tested on a number of images of the knee obtained with a use of the magnetic resonance imaging technique (MR). Images of the knee were collected from healthy subjects and patients with a clinically diagnosed meniscal pathology. Then, watershed technique was compared with other popular methods of image segmentation, i.e. simple thresholding and region growing. For this purpose, the execution speed and the efficiency of the methods were analyzed. Additionally, an automatic detection of the meniscus based on MRI of the knee joint was developed. The solutions were implemented using classical image processing methods in the MATLAB environment with an application of the Image Processing Toolbox and MVtec Halcon vision libraries. The presented methods will have a practical value for the referring physicians and the diagnostic imaging specialists.

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Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines

Cited by 9 publications

References 39 publications

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy

Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease

Image processing in detection of knee joints injuries based on MRI images

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