Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

Multi-Atlas Brain MRI Segmentation with Multiway Cut

10.54294/62rn24 ◽

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.

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Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Journal of Digital Imaging ◽

10.1007/s10278-017-9983-4 ◽

2017 ◽

Vol 30 (4) ◽

pp. 449-459 ◽

Cited By ~ 273

Author(s):

Zeynettin Akkus ◽

Alfiia Galimzianova ◽

Assaf Hoogi ◽

Daniel L. Rubin ◽

Bradley J. Erickson

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Brain Mri ◽

Mri Segmentation ◽

Future Directions

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Fast Brain MRI Segmentation Using a Volumetric Deep Learning Approach

10.32470/ccn.2019.1220-0 ◽

2019 ◽

Author(s):

Dennis Bontempi ◽

Sergio Benini ◽

Alberto Signoroni ◽

Lars Muckli ◽

Michele Svanera

Keyword(s):

Deep Learning ◽

Brain Mri ◽

Learning Approach ◽

Mri Segmentation

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MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors ◽

10.3390/s20113243 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3243 ◽

Cited By ~ 2

Author(s):

Nagaraj Yamanakkanavar ◽

Jae Young Choi ◽

Bumshik Lee

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Brain Structure ◽

State Of The Art ◽

Brain Mri ◽

Mri Segmentation ◽

Learning Methods ◽

The Brain

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

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Brain Tumor Classification and Segmentation using Mask R-CNN

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36440 ◽

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.

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Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

Lecture Notes in Computer Science - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 ◽

10.1007/978-3-030-32248-9_40 ◽

2019 ◽

pp. 356-365 ◽

Cited By ~ 4

Author(s):

Adrian V. Dalca ◽

Evan Yu ◽

Polina Golland ◽

Bruce Fischl ◽

Mert R. Sabuncu ◽

...

Keyword(s):

Deep Learning ◽

Brain Mri ◽

Mri Segmentation ◽

Bayesian Brain ◽

Unsupervised Deep Learning

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210104111218 ◽

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.

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