scholarly journals A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

2019 ◽  
Vol 9 (7) ◽  
pp. 1332
Author(s):  
Zhe Zhang ◽  
Jianhua Song
Keyword(s):  
Contextual Information ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Bias Field ◽  
Correction Method ◽  
Intensity Inhomogeneity ◽  
Mri Segmentation ◽  
Robust Clustering ◽  
Clustering Model ◽  
Central Pixel

The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.

Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

2021 ◽  
pp. 1-12
Author(s):  
Lin Wu ◽  
Tian He ◽  
Jie Yu ◽  
Hang Liu ◽  
Shuang Zhang ◽  
...  
Keyword(s):  
Bias Field ◽  
Correction Method ◽  
Surface Coil ◽  
Intensity Inhomogeneity ◽  
Acquisition Time ◽  
Surface Coils ◽  
In Vivo Experiments ◽  
Inhomogeneity Correction ◽  
Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

scholarly journals MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Ivana Despotović ◽  
Bart Goossens ◽  
Wilfried Philips
Keyword(s):  
Image Segmentation ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Bias Field ◽  
Mri Segmentation ◽  
Anatomical Structures ◽  
Segmentation Methods ◽  
Brain Changes ◽  
The Brain ◽  
Segmentation Problem

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

scholarly journals A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Maria Ines Meyer ◽  
Ezequiel de la Rosa ◽  
Nuno Pedrosa de Barros ◽  
Roberto Paolella ◽  
Koen Van Leemput ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Brain Mri ◽  
Gaussian Mixture Models ◽  
Brain Magnetic Resonance Imaging ◽  
Bias Field ◽  
Brain Structures ◽  
Gaussian Mixture ◽  
Training Data ◽  
Training Dataset ◽  
Clinical Scenarios

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios.

scholarly journals A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 89617-89629
Author(s):  
Yunjie Chen ◽  
Mao Cai ◽  
Xinze Zhou ◽  
Cheng Ning ◽  
Chunzheng Cao ◽  
...  
Keyword(s):  
Spatial Information ◽  
Brain Mri ◽  
Bias Field ◽  
Mri Segmentation ◽  
Information Theoretic ◽  
Field Estimation

scholarly journals Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms

2017 ◽  
Vol 16 (7) ◽  
pp. 7065-7076 ◽  
Author(s):  
AJALA Funmilola Alaba ◽  
AKANDE Noah Oluwatobi ◽  
ADEYEMO Isiaka Akinkunmi ◽  
Ogundokun Roseline Oluwaseun
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
False Negative ◽  
Similarity Index ◽  
Brain Magnetic Resonance Imaging ◽  
Bias Field ◽  
True Negative ◽  
Fuzzy C Means ◽  
Segmentation Technique ◽  
Segmentation Task

Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method

scholarly journals Coexistence of neuronal intranuclear inclusion disease and amyotrophic lateral sclerosis: an autopsy case

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Atsuhiko Sugiyama ◽  
Takahiro Takeda ◽  
Mizuho Koide ◽  
Hajime Yokota ◽  
Hiroki Mukai ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Neurons ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Repeat Expansion ◽  
Cerebellar Hemisphere ◽  
Intranuclear Inclusions ◽  
Intranuclear Inclusion ◽  
Neuronal Intranuclear Inclusion ◽  
Lateral Sclerosis

Abstract Background Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease. Pathologically, it is characterized by eosinophilic hyaline intranuclear inclusions in the cells of the visceral organs as well as central, peripheral, and autonomic nervous system cells. Recently, a GGC repeat expansion in the NOTCH2NLC gene has been identified as the etiopathological agent of NIID. Interestingly, this GGC repeat expansion was also reported in some patients with a clinical diagnosis of amyotrophic lateral sclerosis (ALS). However, there are no autopsy-confirmed cases of concurrent NIID and ALS. Case presentation A 60-year-old Taiwanese woman reported a four-month history of progressive weakness beginning in the right foot that spread to all four extremities. She was diagnosed with ALS because she met the revised El Escorial diagnostic criteria for definite ALS with upper and lower motor neuron involvement in the cervical, thoracic, and lumbosacral regions. She died of respiratory failure at 22 months from ALS onset, at the age of 62 years. Brain magnetic resonance imaging (MRI) revealed lesions in the medial part of the cerebellar hemisphere, right beside the vermis (paravermal lesions). The subclinical neuropathy, indicated by a nerve conduction study (NCS), prompted a potential diagnosis of NIID. Antemortem skin biopsy and autopsy confirmed the coexistence of pathology consistent with both ALS and NIID. We observed neither eccentric distribution of p62-positive intranuclear inclusions in the areas with abundant large motor neurons nor cytopathological coexistence of ALS and NIID pathology in motor neurons. This finding suggested that ALS and NIID developed independently in this patient. Conclusions We describe a case of concurrent NIID and ALS discovered during an autopsy. Abnormal brain MRI findings, including paravermal lesions, could indicate the coexistence of NIID even in patients with ALS showing characteristic clinical phenotypes.

scholarly journals Leveraging 3d Information In Unsupervised Brain Mri Segmentation

2021 ◽  
Author(s):  
Benjamin Lambert ◽  
Maxime Louis ◽  
Senan Doyle ◽  
Florence Forbes ◽  
Michel Dojat ◽  
...  
Keyword(s):  
Brain Mri ◽  
Mri Segmentation ◽  
3D Information
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