scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

scholarly journals Pelizaeus-Merzbacher Disease: A Case Report

2020 ◽  
Author(s):  
Ghazaleh Jamalipour Soufi ◽  
Siavash Iravan
Keyword(s):  
Case Report ◽  
White Matter ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Proteolipid Protein ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Mri ◽  
Normal White Matter ◽  
Pelizaeus Merzbacher Disease ◽  
The Brain

Pelizaeus-Merzbacher Disease (PMD), as a rare genetically x-linked leukodystrophy, is a disorder of proteolipid protein expression in myelin formation. This disorder is clinically presented by neurodevelopmental delay and abnormal pendular eye movements. The responsible gene for this disorder is the proteolipid protein gene (PLP1). Our case was a oneyear-old boy referred to the radiology department for evaluating the Central Nervous System (CNS) development by brain Magnetic Resonance Imaging (MRI). Clinically, he demonstrated neuro-developmental delay symptoms. The brain MRI results indicated a diffuse lack of normal white matter myelination. This case report should be considered about the possibilityof PMD in the brain MRI of patients who present a diffuse arrest of normal white matter myelination.

scholarly journals A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

2021 ◽  
Vol 23 (07) ◽  
pp. 516-529
Author(s):  
Reshma L ◽  
◽  
Sai Priya Nalluri ◽  
Priya R Sankpal ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Support Vector ◽  
Imaging Data ◽  
Diagnosis Of Dementia ◽  
User Friendly ◽  
Clustering Pattern ◽  
Medical Analysis ◽  
The Brain

In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

scholarly journals Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

2020 ◽  
Vol 10 (5) ◽  
pp. 1773 ◽  
Author(s):  
Hafiz Zia Ur Rehman ◽  
Hyunho Hwang ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Approaches ◽  
Diagnostic Systems ◽  
Comprehensive Overview ◽  
Current Trends ◽  
Imaging Protocols ◽  
Skull Stripping ◽  
The Brain ◽  
Brain Parts

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

scholarly journals Transient Periictal Brain Imaging Abnormality in a Saudi Patient with Probable Celiac Disease Epilepsy and Occipital Calcification Syndrome

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Soha Khan ◽  
Asma AlNajjar ◽  
Abdullah Alquaydheb ◽  
Shahpar Nahrir
Keyword(s):  
Celiac Disease ◽  
Energy Demand ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pump Failure ◽  
Mediterranean Countries ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Celiac disease epilepsy and occipital calcification (CEC) syndrome is a rare, emerging disease first described in 1992. To date, fewer than 200 cases have been reported worldwide. CEC syndrome is generally thought to be a genetic, noninherited, and ethnically and geographically restricted disease in Mediterranean countries. However, we report the first ever case of probable CEC in a Saudi patient. Furthermore, the patient manifested a magnitude of brain magnetic resonance imaging (MRI) signal abnormalities during the periictal period which, to the best of our knowledge, has never been described in CEC. The brain MRI revealed diffusion-weighted imaging (DWI) restriction with a concordant area of apparent diffusion coefficient (ADC) hypointensity around bilateral occipital area of calcification. An imbalance between the heightened energy demand during ictal phase of the seizure and unadjusted blood supply may have caused an electric pump failure and cytotoxic edema, which then led to DWI/ADC signal alteration.

scholarly journals SEMIAUTOMATIC DETECTION OF TUMORAL ZONE

2011 ◽  
Vol 21 (1) ◽  
pp. 13 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Brain Magnetic Resonance Imaging ◽  
Fuzzy Classification ◽  
Robust Method ◽  
Resonance Imaging ◽  
Segmentation Algorithms ◽  
Different Types ◽  
Magnetic Resonance Imaging Mri ◽  
Types Of Information ◽  
The Brain ◽  
Fuzzy C Means Algorithm

This paper describes a robust method based on the cooperation of fuzzy classification and regions segmentation algorithms, in order to detect the tumoral zone in the brain Magnetic Resonance Imaging (MRI). On one hand, the classification in fuzzy sets is done by the Fuzzy C-Means algorithm (FCM), where a study of its different parameters and its complexity has been previously realised, which led us to improve it. On the other hand, the segmentation in regions is obtained by an hierarchical method through adaptive thresholding. Then, an operator expert selects a germ in the tumoral zone, and the class containing the sick zone is localised in return for the FCM algorithm. Finally, the superposition of the two partitions of the image will determine the sick zone. The originality of our approach is the parallel exploitation of different types of information in the image by the cooperation of two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.

scholarly journals Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1964
Author(s):  
Reza Kalantar ◽  
Gigin Lin ◽  
Jessica M. Winfield ◽  
Christina Messiou ◽  
Susan Lalondrelle ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Technological Development ◽  
Automatic Segmentation ◽  
Research Attention ◽  
Segmentation Algorithms ◽  
Rectal Cancers ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

Brain MRI in Inherited Metabolic Diseases of Adulthood

2016 ◽  
Author(s):  
Alessandro Burlina ◽  
Renzo Manara
Keyword(s):  
Magnetic Resonance Imaging ◽  
Metabolic Diseases ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Diagnostic Process ◽  
Resonance Imaging ◽  
Inherited Metabolic Diseases ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain magnetic resonance imaging (MRI) is an important tool to investigate inherited metabolic diseases in adulthood. In the present chapter the major neuroradiological findings that brain MRI can provide to adult metabolic clinicians will be presented, classified according to white and gray matter involvement.The role of brain MRI in the diagnostic process and clinical monitoring of specific inherited metabolic affecting the brain will be examined.

scholarly journals Neuroimaging predictors of structural epilepsy in the COVID-19 catamnesis

2021 ◽  
Vol 13 (3) ◽  
pp. 274-285
Author(s):  
N. E. Maslov ◽  
N. V. Yuryeva ◽  
E. I. Khamtsova ◽  
A. A. Litvinova
Keyword(s):  
Nervous System ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Clinical Disorder ◽  
Brain Substance ◽  
Coronavirus Infection ◽  
Magnetic Resonance Imaging Mri ◽  
Structural Epilepsy ◽  
Novel Coronavirus ◽  
The Brain

Respiratory system pathology is the most common clinical disorder associated with COVID-19. However, there are also lesions of the immune, cardiovascular, genitourinary, endocrine systems, and digestive tract. In addition, there are numerous reports on infection-related neurological manifestations, which can be divided into 3 groups: central nervous system manifestations (headache and dizziness, stroke, encephalopathy, encephalitis, acute myelitis), lesions of the peripheral nervous system (anosmia, Guillain–Barre syndrome), secondary lesions in the skeletal muscles. Brain damage that occurs during novel coronavirus infection and determines some of the above-mentioned manifestations often account for the development of structural epilepsies. Only a few scarce review articles on neuroimaging features in patients with COVID-19 have been found in Russian research publications.The objective of the review was to collect, analyze and summarize the results of brain magnetic resonance imaging (MRI), currently accumulated worldwide in patients with COVID-19. We present the most common diagnoses based on brain MRI in patients with COVID-19 established by foreign researchers from March 2020 to March 2021, as well as initial attempts to interpret the pathophysiological mechanisms of the changes observed in the brain substance.

scholarly journals 3D U-Net improves automatic brain extraction for isotropic rat brain MRI data

2021 ◽  
Author(s):  
Li-Ming Hsu ◽  
Shuai Wang ◽  
Lindsay Walton ◽  
Tzu-Wen Winnie Wang ◽  
Sung-Ho Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Rat Brain ◽  
Spatial Resolution ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Superior Performance ◽  
Tissue Segmentation ◽  
Convolutional Network ◽  
Brain Extraction ◽  
Rodent Brain

AbstractBrain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation (RATS), Pulse-Coupled Neural Network (PCNN), SHape descriptor selected External Regions after Morphologically filtering (SHERM), and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community.Significant methodological contributionWe proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

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