scholarly journals Multi-Atlas Brain MRI Segmentation with Multiway Cut

2013 ◽  
Author(s):  
Duygu Sarikaya ◽  
Liang Zhao ◽  
Jason J. Corso
Keyword(s):  
Brain Mri ◽  
Brain Structures ◽  
Mri Segmentation ◽  
Weighted Voting ◽  
Brain Images ◽  
Anatomical Structures ◽  
Test Dataset ◽  
Multiway Cut ◽  
The Subject

Characterization of anatomical structure of the brain and efficient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical structures of magnetic resonance human brain images. Our method uses the prior knowledge of labels given by experts to statistically investigate the spatial correspondences of brain structures in subject images. We create a multi-atlas by registering the training images to the subject image and then propagating corresponding labels to the graph of the image. Label fusion then combines these multiple labels of atlases into one label at each voxel with intensity similarity based weighted voting. Finally we cluster the graph using multiway cut in order to achieve the final 3D segmentation of the subject image. The promising evaluation results of our segmentation method on the MRBrainS13 Test Dataset shows the efficiency and robustness of our algorithm.

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.

Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

2017 ◽  
pp. 223-242
Author(s):  
Akshay Pai ◽  
Yuan-Ching Teng ◽  
Joseph Blair ◽  
Michiel Kallenberg ◽  
Erik B. Dam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Mri Segmentation

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 Brain Tumor Classification and Segmentation using Mask R-CNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 667-668
Author(s):  
Kishore D
Keyword(s):  
Brain Tumor ◽  
Human Error ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Manual Segmentation ◽  
Brain Disorders ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
High Consistency

MRI segmentation is a crucial task in many clinical applications. A variety of approaches for brain analysis rely on accurate segmentation of anatomical regions. Quantitative analysis of brain MRI has been used extensively for the characterization of brain disorders such as Alzheimer’s, epilepsy, schizophrenia, multiple sclerosis, cancer, and many infectious, degenerative diseases. Manual Segmentation requires outlining structures slice-by-slice, it is not only expensive and tedious but also inaccurate due to human error. Also, manual segmentation is extremely time-consuming and initial hours of brain tumor and strokes are crucial to diagnose it. Therefore, automated segmentation procedures are needed to ensure accuracy close to that of experts with high consistency. We propose to create a Deep Learning based Brain Segmentation solution that would fully automate the process of Brain Tumor Segmentation to solve those cases which are generally missed by the human eye and save time.

The Image of the Accountant in a German Context

2004 ◽  
Vol 4 (1) ◽  
pp. 62-89 ◽  
Author(s):  
Andreas Hoffjan
Keyword(s):  
Content Analysis ◽  
Empirical Study ◽  
Cost Reduction ◽  
Maximum Degree ◽  
Work Ethic ◽  
Personal Characteristics ◽  
Management Accountant ◽  
The Subject ◽  
Professional Context

This study introduces content analysis as a method of examining the accountant's role. The empirical study is based on 73 advertisements, which are directed primarily at employees who are affected by the management accountant's work. The findings of the study indicate that the subject of accountancy is used particularly in connection with promises of “cost reduction.” Consequently, the majority of advertisements use the accountant stereotype of “savings personified.” In a professional context, the work ethic of the management accountant is given particular emphasis in the advertisements. He/she identifies him/herself with his/her task to the maximum degree, is regarded as loyal to his/her company and, for the most part, is well organized in his/her work. However, the characterization of the management accountant as a well disciplined company-person conflicts with the negative portrayal of his/her professional qualities. In advertisements, the management accountant is portrayed as a rather inflexible, passive, and uncreative specialist who, as a result of these qualities, often demotivates others. The personal characteristics of the management accountant are shown in a negative light. This gives him/her the unappealing image of a humorless, envious, dissociated, and ascetic corporate-person.

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 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

Left ventricular dysfunction is associated with cerebral grey matter injury: An in-vivo brain MRI segmentation study

2012 ◽  
Vol 321 (1-2) ◽  
pp. 111-113 ◽  
Author(s):  
Pratik Bhattacharya ◽  
Fen Bao ◽  
Megha Shah ◽  
Gautam Ramesh ◽  
Ramesh Madhavan ◽  
...  
Keyword(s):  
Left Ventricular Dysfunction ◽  
Grey Matter ◽  
Ventricular Dysfunction ◽  
Brain Mri ◽  
Left Ventricular ◽  
Mri Segmentation ◽  
Cerebral Grey Matter ◽  
Grey Matter Injury

scholarly journals Axiomatization and Measurement of Quasi-Hyperbolic Discounting *

2014 ◽  
Vol 129 (3) ◽  
pp. 1449-1499 ◽  
Author(s):  
José Luis Montiel Olea ◽  
Tomasz Strzalecki
Keyword(s):  
Hyperbolic Discounting ◽  
Partial Identification ◽  
Axiomatic Characterization ◽  
Discount Factors ◽  
Elasticity Of Intertemporal Substitution ◽  
Trade Offs ◽  
Subject Pool ◽  
Identification Approach ◽  
The Subject

Abstract This article provides an axiomatic characterization of quasi-hyperbolic discounting and a more general class of semi-hyperbolic preferences. We impose consistency restrictions directly on the intertemporal trade-offs by relying on what we call “annuity compensations.” Our axiomatization leads naturally to an experimental design that disentangles discounting from the elasticity of intertemporal substitution. In a pilot experiment we use the partial identification approach to estimate bounds for the distributions of discount factors in the subject pool. Consistent with previous studies, we find evidence for both present and future bias.

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