Segmentation of Gray Matter, White Matter and Brain Tumour from Brain MR Images

2014 ◽  
Vol 7 (2) ◽  
pp. 79-85
Author(s):  
Ms. V. Kavitha ◽  
◽  
Mr.S. Rajesh Kumar Reddy
Keyword(s):  
White Matter ◽  
Brain Tumour ◽  
Gray Matter ◽  
Mr Images ◽  
Brain Mr Images

Segmentation of Rat Brain MR Images Using Artificial Neural Network Classifier

2013 ◽  
Author(s):  
K. N. Magdoom ◽  
Thomas H. Mareci ◽  
Malisa Sarntinoranont
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Rat Brain ◽  
Gray Matter ◽  
Computational Models ◽  
Chemotherapeutic Agents ◽  
Mr Images ◽  
Artificial Neural Network Classifier ◽  
Brain Mr Images ◽  
The Brain

Recently MR image based computational models are being developed to assist targeted drug delivery in the brain by helping determine appropriate catheter position, drug dose among others to achieve the desired drug distribution [1–3]. Such a planning might be important to prevent damaging healthier tissues because many of the drugs (e.g. chemotherapeutic agents) are usually toxic and needs to be concentrated in specific regions of interest (e.g. tumor). However, for the image based model to make accurate predictions, it is important to segment the image and assign appropriate tissue properties such as hydraulic conductivity which are known to vary significantly within the brain. For example, it has been experimentally found that drugs injected into brain parenchyma get preferentially transported along the white matter tracts compared to the gray matter regions [4]. Segmenting MR images is a challenging task since the pixel intensities between different regions often overlap, hence traditional approaches based on thresholds might not provide reliable results. In this study, we used multi-layered perceptron (MLP) neural network to segment rat brain MR images into 3 different regions namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

2019 ◽  
Vol 31 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Yogita Dubey ◽  
Milind Mushrif ◽  
Kajal Mitra
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
White Matter ◽  
Rough Set ◽  
Gray Matter ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Cystic Component ◽  
Brain Mr Images

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

Atlas-based segmentation of neonatal brain MR images using a gray matter enhancing step

2017 ◽  
Vol 12 (4) ◽  
pp. 633-640
Author(s):  
Ulises Rodríguez-Domínguez ◽  
Oscar Dalmau ◽  
Omar Ocegueda ◽  
Jorge Bosch-Bayard
Keyword(s):  
Gray Matter ◽  
Mr Images ◽  
Neonatal Brain ◽  
Brain Mr Images

scholarly journals Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Zhong ◽  
David Utriainen ◽  
Ying Wang ◽  
Yan Kang ◽  
E. Mark Haacke
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Gray Matter ◽  
Signal Intensity ◽  
High Spatial Frequency ◽  
Inversion Recovery ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Automated Algorithm ◽  
Fluid Attenuated Inversion Recovery

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to beY=1.04X+1.74  (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH.

Interactive segmentation of cerebral gray matter, white matter, and CSF: Photographic and MR images

1994 ◽  
Vol 18 (6) ◽  
pp. 449-460 ◽  
Author(s):  
Thad Q. Bartlett ◽  
Michael W. Vannier ◽  
Daniel W. McKeel ◽  
Mokhtar Gado ◽  
Charles F. Hildebolt ◽  
...  
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Mr Images ◽  
Interactive Segmentation

scholarly journals Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

2021 ◽  
pp. 102215
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Nicola K. Dinsdale ◽  
Peter M. Rothwell ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
White Matter ◽  
Domain Adaptation ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Brain Mr Images

scholarly journals Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

2021 ◽  
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Nicola K. Dinsdale ◽  
Peter M. Rothwell ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
White Matter ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Target Domain ◽  
Baseline Model ◽  
Adversarial Learning ◽  
Adversarial Training ◽  
Brain Mr Images

AbstractRobust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.

scholarly journals Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

2020 ◽  
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Peter M. Rothwell ◽  
Mark Jenkinson ◽  
Ludovica Griffanti
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Training Data ◽  
Periventricular White Matter ◽  
Mr Images ◽  
Comparable Performance ◽  
Brain Mr Images ◽  
Reliable Quantification ◽  
Anatomical Information ◽  
Better Than

AbstractWhite matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.

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