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A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images [article]

Yunzhe Xue, Meiyan Xie, Fadi G. Farhat, Olga Boukrina, A. M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves
2019 arXiv   pre-print
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images.  ...  Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules  ...  Methods Fully 3D multi-path convolutional neural network Our contribution is a fully 3D convolutional network for predicting stroke lesions in brain MRI images.  ... 
arXiv:1907.07807v2 fatcat:a23pwviv35hj5jnq5hagiuzvby

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

Yunzhe Xue, Fadi G. Farhat, Olga Boukrina, A.M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves
2019 NeuroImage: Clinical  
We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation.  ...  Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning.  ...  Conclusion We have presented a multi-path, multi-modal convolutional neural network system for identifying lesions in brain MRI images. Our method is fully automatic.  ... 
doi:10.1016/j.nicl.2019.102118 pmid:31865021 pmcid:PMC6931186 fatcat:7kfj6wbnindzphdqwkx7ally5m

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics.  ...  A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study.  ...  Cascade's fully convolution neural network is an effective method for image segmentation that splits multi-model MRIs into subhierarchy regions. 3D SE-inception network employs the 3D multi-model image  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Manoj V. Khatokar, M. Hemanth Kumar, K. Chandrahas, M. D. Swetha, Preeti Satish
2021 Asian Journal of Computer Science and Technology  
In this paper, the latest methodologies regarding the identification of the MS lesions in MRI scans like T2 FLAIR or DTI, using automated techniques like deep learning, computer vision, neural network  ...  It would identify the MS lesion in DTI scan and eventually highlight that lesion position in the T2 image scan. This would help radiologist in a way to effectively handle multiple MRI scans.  ...  This work depicted that the conventional method of usage of a fully convolutional neural network (F-CNN) with a 3D U-Net framework failed to point out the smaller regions of cortical lesions in the brain  ... 
doi:10.51983/ajcst-2021.10.1.2699 fatcat:s6fvc37dpnfpnknma2jcd22bqu

Prediction of Stroke lesion at 90-day follow-up by fusing raw DSC-MRI with parametric maps using Deep Learning

Adriano Pinto, Joana Amorim, Arsany Hakim, Victor Alves, Mauricio Reyes, Carlos A. Silva
2021 IEEE Access  
Combining both data types in a single architecture, with dedicated paths, we achieve competitive results when predicting the final stroke infarct core lesion in the publicly available ISLES 2017 dataset  ...  We aim to automatically extract features from the raw perfusion DSC-MRI to further complement the information gleaned from standard parametric maps, and to overcome the loss of information that can occur  ...  However, we note that Kwon 554 et al. used an ensemble of 12 neural networks combining 555 Fully Convolutional Neural Networks (FCNNs) and Fully-556 Connected Networks (FCNs), while our proposal is a single  ... 
doi:10.1109/access.2021.3058297 fatcat:ffd2xppc6zecvpk6oj2xwz2ize

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  
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  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.  ...  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

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

Batyrkhan Omarov, Azhar Tursynova, Octavian Postolache, Khaled Gamry, Aidar Batyrbekov, Sapargali Aldeshov, Zhanar Azhibekova, Marat Nurtas, Akbayan Aliyeva, Kadrzhan Shiyapov
2022 Computers Materials & Continua  
The modified 3D UNet model proposed by us uses an efficient averaging method inside a neural network.  ...  In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images.  ...  In a convolutional neural network, a small weight matrix is used in convolution operations, which is "moved" along the entire processed layer (at the network input, directly along with the input image)  ... 
doi:10.32604/cmc.2022.020998 fatcat:d2yr52qtvfhkfceyxkwvhugtsy

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI

Stefan Winzeck, Arsany Hakim, Richard McKinley, José A. A. D. S. R. Pinto, Victor Alves, Carlos Silva, Maxim Pisov, Egor Krivov, Mikhail Belyaev, Miguel Monteiro, Arlindo Oliveira, Youngwon Choi (+27 others)
2018 Frontiers in Neurology  
Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging.  ...  A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016.  ...  A.2.1.1. Acknowledgments We would like the acknowledge the GPU computing resources provided by the MGH and BWH Center for Clinical Data Science. A.2.10.1. Acknowledgments  ... 
doi:10.3389/fneur.2018.00679 pmid:30271370 pmcid:PMC6146088 fatcat:fpjctcngobblpkzeror2fpsncu

3D Deep Learning on Medical Images: A Review [article]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 arXiv   pre-print
This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis.  ...  We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.  ...  [92] proposed multi-scale prediction with a fusion scheme for 3D CNN (Figure 7) .  ... 
arXiv:2004.00218v4 fatcat:iucszcjffnbwbbzc4zzqpbvahy

Using a patch-wise M-net convolutional neural network for tissue segmentation in brain MRI images

Nagaraj Yamanakkanavar, Bumshik Lee
2020 IEEE Access  
[38] proposed a multi-model, multi-size, and multi-view deep neural network for the segmentation brain MRI on a sliceby-slice basis.  ...  [17] used deep fully convolutional networks to train a model for multiple image modalities, such as T1, T2, and FA, and then combine layered feature maps in a final segmentation map output.  ... 
doi:10.1109/access.2020.3006317 fatcat:xg32a6nv3vakbm6mvtzuhblslu

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Tanzila Saba
2020 Journal of Infection and Public Health  
Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure.  ...  Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes.  ...  Acknowledgements This work was supported by the research Project [Brain Tumor Detection and Classification using 3D CNN and Feature Selection Architecture]; Prince Sultan University; Saudi Arabia [SEED-CCIS  ... 
doi:10.1016/j.jiph.2020.06.033 pmid:32758393 fatcat:sglazth4znh5jjtozguaktruce

3D Deep Learning on Medical Images: A Review

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 Sensors  
This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis.  ...  We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.  ...  [92] proposed multi-scale prediction with a fusion scheme for 3D CNN (Figure 7 ).  ... 
doi:10.3390/s20185097 pmid:32906819 pmcid:PMC7570704 fatcat:top2ambpizdzdpsqamz2xm643u

A Review on Computer Aided Diagnosis of Acute Brain Stroke

Mahesh Anil Inamdar, Udupi Raghavendra, Anjan Gudigar, Yashas Chakole, Ajay Hegde, Girish R. Menon, Prabal Barua, Elizabeth Emma Palmer, Kang Hao Cheong, Wai Yee Chan, Edward J. Ciaccio, U. Rajendra Acharya
2021 Sensors  
status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation  ...  , in permanently damaged brain tissue.  ...  ., proposed a multi-modal multi-path convolutional neural network system for automating stroke area segmentation by analyzing brain-behavior relationships, thereby eliminating the need for manual segmentation  ... 
doi:10.3390/s21248507 pmid:34960599 pmcid:PMC8707263 fatcat:zc4gtjhkoje2jotcqr5gvlatu4

Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

Shahab Aslani, Michael Dayan, Loredana Storelli, Massimo Filippi, Vittorio Murino, Maria A. Rocca, Diego Sona
2019 NeuroImage  
Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data.  ...  In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images.  ...  We designed an end-to-end encoder-decoder network including a multi-branch downsampling path as the encoder, a multi-scale feature fusion and the multi-scale upsampling blocks as the decoder.  ... 
doi:10.1016/j.neuroimage.2019.03.068 pmid:30953833 fatcat:f3gl3sgqbfevxp4jit6muvr5yq
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