Segmentation of MR images of degenerative diseases by automated method using ANN technique

2013 ◽  
Author(s):  
G. Paul
Keyword(s):  
Degenerative Diseases ◽  
Mr Images ◽  
Automated Method

Automated Method for Identification of Patients With Alzheimer’s Disease Based on Three-dimensional MR Images

2008 ◽  
Vol 15 (3) ◽  
pp. 274-284 ◽  
Author(s):  
Hidetaka Arimura ◽  
Takashi Yoshiura ◽  
Seiji Kumazawa ◽  
Kazuhiro Tanaka ◽  
Hiroshi Koga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Mr Images ◽  
Automated Method

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease

2009 ◽  
Vol 110 (2) ◽  
pp. 208-219 ◽  
Author(s):  
Eric Bardinet ◽  
Manik Bhattacharjee ◽  
Didier Dormont ◽  
Bernard Pidoux ◽  
Grégoire Malandain ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Basal Ganglia ◽  
Parkinson Disease ◽  
Brain Stimulation ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Registration Method ◽  
Automated Method ◽  
The Brain ◽  
Deep Brain

Object The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Methods Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. Conclusions This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Development of an automated method for detection of lacunar infarct regions on brain MR images

2005 ◽  
Vol 1281 ◽  
pp. 1407 ◽  
Author(s):  
R. Yokoyama ◽  
A. Matsui ◽  
H. Fujita ◽  
T. Hara ◽  
X. Zhou ◽  
...  
Keyword(s):  
Lacunar Infarct ◽  
Mr Images ◽  
Automated Method ◽  
Brain Mr Images

Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images

2007 ◽  
Vol E90-D (6) ◽  
pp. 943-954 ◽  
Author(s):  
R. YOKOYAMA ◽  
X. ZHANG ◽  
Y. UCHIYAMA ◽  
H. FUJITA ◽  
T. HARA ◽  
...  
Keyword(s):  
Lacunar Infarct ◽  
Mr Images ◽  
Automated Method ◽  
Brain Mr Images

Comparison of Total Kidney Volume Quantification Methods in Autosomal Dominant Polycystic Disease for a Comprehensive Disease Assessment

2017 ◽  
Vol 45 (5) ◽  
pp. 373-379 ◽  
Author(s):  
Dario Turco ◽  
Marco Busutti ◽  
Renzo Mignani ◽  
Riccardo Magistroni ◽  
Cristiana Corsi
Keyword(s):  
Autosomal Dominant ◽  
Contour Detection ◽  
Disease Stage ◽  
Disease Assessment ◽  
Patient Specific ◽  
Kidney Volume ◽  
Mr Images ◽  
Total Kidney Volume ◽  
Performance Volume ◽  
Automated Method

Background: In recent times, the scientific community has been showing increasing interest in the treatments aimed at slowing the progression of the autosomal dominant polycystic kidney disease (ADPKD). Therefore, in this paper, we test and evaluate the performance of several available methods for total kidney volume (TKV) computation in ADPKD patients - from echography to MRI - in order to optimize patient classification. Methods: Two methods based on geometric assumptions (mid-slice [MS], ellipsoid [EL]) and a third one on true contour detection were tested on 40 ADPKD patients at different disease stage using MRI. The EL method was also tested using ultrasound images in a subset of 14 patients. Their performance was compared against TKVs derived from reference manual segmentation of MR images. Patient clinical classification was also performed based on computed volumes. Results: Kidney volumes derived from echography significantly underestimated reference volumes. Geometric-based methods applied to MR images had similar acceptable results. The highly automated method showed better performance. Volume assessment was accurate and reproducible. Importantly, classification resulted in 79, 13, 10, and 2.5% of misclassification using kidney volumes obtained from echo and MRI applying the EL, the MS and the highly automated method respectively. Conclusion: Considering the fact that the image-based technique is the only approach providing a 3D patient-specific kidney model and allowing further analysis including cyst volume computation and monitoring disease progression, we suggest that geometric assumption (e.g., EL method) should be avoided. The contour-detection approach should be used for a reproducible and precise morphologic classification of the renal volume of ADPKD patients.

The learning-based automatic segmentation algorithm of brain MR images based on 7T

2020 ◽  
Vol 16 ◽  
Author(s):  
Minghui Deng ◽  
Zhenhao Jin ◽  
Ran Yu ◽  
Qingshuang Zeng
Keyword(s):  
Automatic Segmentation ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Image Intensity ◽  
Automated Method ◽  
Brain Mr Images ◽  
Intensity Contrast

Background: The learning-based algorithms provide an ability to automatically estimate and refine GM, WM and CSF. The ground truth manually achieved from the 3T MR image may not be accurate and reliable with poor image intensity contrast. It will seriously influence the classification performance because the supervised learning-based algorithms extremely rely on the ground truth. Recently, the 7T MR images brings about the excellent image intensity contrast, while Structured Random Forest (SRF) performs the pixel-level classification and achieves structural and contextual information in images. Materials and Methods: In this paper, a automatic segmentation algorithm is proposed based on ground truth achieved by the corresponding 7T subjects for segmenting the 3T&1.5T brain tissues using SRF classifiers. Through taking advantage of the 7T brain MR images, we can achieve the highly accuracy and reliable ground truth and then implement the training of SRF classifiers. Our proposed algorithm effectively integrates the T1-weighed images along with the probability maps to train the SRF classifiers for brain tissue segmentation. Results: Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 95.14%±0.9%, 90.17%±1.83%, and 81.96%± 4.32% for WM, GM, and CSF. With the experiment results, the proposed algorithm can achieve better performances than other automatic segmentation methods. Further experiments are performed on the 200 3T&1.5T brain MR images of ADNI dataset and our proposed method shows promised performances. Conclusions: The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.

Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images

2020 ◽  
Vol 16 (5) ◽  
pp. 469-478
Author(s):  
Hsian-Min Chen ◽  
Clayton Chi-Chang Chen ◽  
Hsin Che Wang ◽  
Yung-Chieh Chang ◽  
Kuan-Jung Pan ◽  
...  
Keyword(s):  
White Matter ◽  
Anomaly Detection ◽  
White Matter Hyperintensities ◽  
Similarity Index ◽  
White Matter Lesions ◽  
Cerebral White Matter ◽  
Mr Images ◽  
Detection Algorithms ◽  
Automated Method ◽  
Brain Mr Images

Background: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients’ conditions can be contained. Aims: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. Methods: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. Results: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. Conclusion: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

scholarly journals Automated Electrodes Detection during simultaneous EEG/fMRI

2018 ◽  
Author(s):  
Mathis Fleury ◽  
Christian Barillot ◽  
Marsel Mano ◽  
Elise Bannier ◽  
Pierre Maurel
Keyword(s):  
Magnetic Resonance ◽  
Brain Activity ◽  
Detection Accuracy ◽  
Source Reconstruction ◽  
Mr Images ◽  
Automated Method ◽  
Accurate Knowledge ◽  
Fully Automatic ◽  
Short Echo Time ◽  
Average Position

1AbstractThe coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localisation of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localise EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure.

scholarly journals A fully automated method for quantifying and localizing white matter hyperintensities on MR images

2006 ◽  
Vol 148 (2-3) ◽  
pp. 133-142 ◽  
Author(s):  
Minjie Wu ◽  
Caterina Rosano ◽  
Meryl Butters ◽  
Ellen Whyte ◽  
Megan Nable ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Mr Images ◽  
Automated Method
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