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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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]

Yushan Feng, Huitong Pan, Craig Meyer, Xue Feng
2018 arXiv   pre-print
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)

Eleftherios Kontopodis, Efrosini Papadaki, Eleftherios Trivzakis, Thomas Maris, Panagiotis Simos, Georgios Papadakis, Aristidis Tsatsakis, Demetrios Spandidos, Apostolos Karantanas, Kostas Marias
2021 Experimental and Therapeutic Medicine  
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]

Huahong Zhang, Ipek Oguz
2020 arXiv   pre-print
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]

Yang Ma, Chaoyi Zhang, Mariano Cabezas, Yang Song, Zihao Tang, Dongnan Liu, Weidong Cai, Michael Barnett, Chenyu Wang
2022 arXiv   pre-print
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

Chenyi Zeng, Lin Gu, Zhenzhong Liu, Shen Zhao
2020 Frontiers in Neuroinformatics  
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]

Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour
2018 IEEE Access   accepted
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]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
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]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
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]

Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari, Parisa Moridian, Mitra Rezaei, Roohallah Alizadehsani, Fahime Khozeimeh, Juan Manuel Gorriz, Jónathan Heras, Maryam Panahiazar, Saeid Nahavandi, U. Rajendra Acharya
2021 arXiv   pre-print
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

Mehdi SadeghiBakhi, Hamidreza Pourreza, Hamidreza Mahyar
2022 IEEE Journal of Translational Engineering in Health and Medicine  
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

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
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

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
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

U. Raghavendra, U. Rajendra Acharya, Hojjat Adeli
2019 European Neurology  
, 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

Xiangbin Liu, Liping Song, Shuai Liu, Yudong Zhang
2021 Sustainability  
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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