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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
with Threshold Loss for Cancer Detection in Automated Breast Ultrasound 316 SPNet: Shape Prediction using a Fully Convolutional Neural Network 317 Modeling Longitudinal Voxel-wise Feature Change in Normal ...
and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound 353 Roto-Translation ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
A Self-Adaptive Network For Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Protocols
[article]
2018
arXiv
pre-print
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Deep neural networks (DNN) have shown promises in the lesion segmentation of multiple sclerosis (MS) from multicontrast MRI including T1, T2, proton density (PD) and FLAIR sequences. ...
Experiments were performed using the IEEE ISBI 2015 Longitudinal MS Lesion Challenge dataset and our method is currently ranked 2nd with a Dice similarity coefficient of 0.684. ...
Recently deep learning methods have been developed for MS lesion segmentation from multi-contrast MRI, including Recurrent Neural Networks (RNN) [4] , Fully Convolutional Neural Networks (FCNN) [5] ...
arXiv:1811.07491v1
fatcat:lxpns3pxkbaonp7aye7hmbq6ke
Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)
2021
Experimental and Therapeutic Medicine
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Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. ...
Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification ...
on Biomedical Imaging; TPR, true positive rate; FCNN, fully convolutional neural network; MSSEG, MS lesion segmentation challenge; RRMS, relapsing-remitting multiple sclerosis; SN, sensitivity; MRI, magnetic ...
doi:10.3892/etm.2021.10583
pmid:34504594
pmcid:PMC8393268
fatcat:yolthcmsgfhdbbwwpcn5nte2ly
Multiple Sclerosis Lesion Segmentation – A Survey of Supervised CNN-Based Methods
[article]
2020
arXiv
pre-print
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Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. ...
In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. ...
This work was supported, in part, by the NIH grant R01-NS094456 and National Multiple Sclerosis Society award PP-1905-34001. ...
arXiv:2012.08317v2
fatcat:2usedwsl2bbe5gbp35e5blnf3q
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
[article]
2022
arXiv
pre-print
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Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically ...
Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. ...
A gated recurrent unit (GRU) [74] is a feature extraction module based on convolution and a gating mechanism, working as a building block for recurrent neural networks (RNN). ...
arXiv:2104.10029v3
fatcat:elds3foafrdc5ireld5wahp5ra
Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI
2020
Frontiers in Neuroinformatics
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In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. ...
In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). ...
Zhang et al. (2019b) proposes a recurrent slice-wise attention network by repeatedly using the contextual information of MS lesions to respond to the problem that Recurrent Neural Network (RNN) and long ...
doi:10.3389/fninf.2020.610967
pmid:33328949
pmcid:PMC7714963
fatcat:ed6xnxmsmnbarklatz45vrzrce
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
[article]
2018
IEEE Access
accepted
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Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. ...
In this work we trained fully convolutional deep neural networks using an asymmetric similarity loss function to mitigate the issue of data imbalance and achieve much better tradeoff between precision ...
Among these, a recurrent neural network with DropConnect [24] and a cascaded convolutional neural network (CNN) [25] based on a cascade of two 3D patch-wise CNNs achieved remarkable results in MS lesion ...
doi:10.1109/access.2018.2886371
pmid:31528523
pmcid:PMC6746414
arXiv:1803.11078v4
fatcat:i4ctg3fnrvgabjjwmxxqfpfcfm
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
[chapter]
2018
Lecture Notes in Computer Science
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Multiple
Instance Decisions Aggregated CNN
Mohammad Arafat Hussain*; Ghassan Hamarneh; Rafeef Abugharbieh
T-40
Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver ...
Activation Estimation in Brain Networks During Task and Rest Using BOLD-fMRI Michael Hutel*; Andrew Melbourne; Sebastien Ourselin T-103 Identifying Brain Networks of Multiple Time Scales via Deep Recurrent ...
T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen ...
doi:10.1007/978-3-030-00928-1_1
fatcat:ypoj3zplm5awljf6u5c2spgiea
Deep Learning Based Brain Tumor Segmentation: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.09479v2 fatcat:aonucqiwkbclxkjt4lyqckzsmyOther Versions
More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality ...
A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. ...
[28] used fully convolutional networks with skip connections to segment multiple sclerosis lesions. Isensee et al. ...
arXiv:2007.09479v3
fatcat:vdbpwfdsorfudkvnvottexd7je
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
[article]
2021
arXiv
pre-print
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2021 accepted arXiv:2105.04881v2 fatcat:xr5lzcfyjffppmbdi6tljykcve
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. ...
MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. ...
These permanent changes are called sclerosis, and because these lesions occur in multiple and different areas, the disease is called multiple sclerosis [5, 6] . ...
arXiv:2105.04881v1
fatcat:ulhvw5lfafgtrhmunjv4kplf2e
Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images
2022
IEEE Journal of Translational Engineering in Health and Medicine
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Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and spatial-channel attention module, to segment MS lesions. ...
Objective: Multiple Sclerosis (MS) is an autoimmune and demyelinating disease that leads to lesions in the central nervous system. ...
Recently, convolution neural network strategies have reported outstanding performance in medical image processing, especially in MS lesion segmentation. ...
doi:10.1109/jtehm.2022.3172025
fatcat:eke3flvnvnge3lw6qq3563vjke
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. ...
The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. ...
Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article. ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
2019
European Neurology
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, multiple sclerosis, and ischemic brain stroke using physiological signals and images. ...
Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively. ...
neural network; MS, multiple sclerosis; SVM, support vector ma-
chine; kNN, k-nearest neighbour. ...
doi:10.1159/000504292
pmid:31743905
fatcat:frg5lwwt7vauxm6rjgc7sepy6y
A Review of Deep-Learning-Based Medical Image Segmentation Methods
2021
Sustainability
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With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. ...
Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. ...
OASIS-3 is a longitudinal neuroimaging, clinical, cognitive, and biomarker data set for normal aging and Alzheimer's disease. ...
doi:10.3390/su13031224
fatcat:pn2qbyv53zbuhhiuem2pc4dg3u
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