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A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Abang Mohd Arif Anaqi Abang Isa, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS, Kota Samarahan, 94300 Sarawak, MALAYSIA, Kuryati Kipli, Muhammad Hamdi Mahmood, Ahmad Tirmizi Jobli, Siti Kudnie Sahari, Mohd Saufee Muhammad, Soon K Chong, Buthainah Nawaf Issa AL-Kharabsheh, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS, Kota Samarahan, 94300 Sarawak, MALAYSIA, Faculty of Medicine and Health Sciences, University Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, MALAYSIA, Faculty of Medicine and Health Sciences, University Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, MALAYSIA (+4 others)
2020 International Journal of Integrated Engineering  
The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only.  ...  The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction.  ...  Acknowledgement The author, Abang Mohd Arif Anaqi would like to acknowledge the support from Yayasan Sarawak for the financial support through the Yayasan Tun Taib (Research) scholarship.  ... 
doi:10.30880/ijie.2020.12.06.014 fatcat:fc2523wverb5jht4nc6vehdlvq

Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps

Arsany Hakim, Benjamin Messerli, Raphael Meier, Tomas Dobrocky, Sebastian Bellwald, Simon Jung, Richard McKinley, Roland Wiest
2021 Clinical and Translational Neuroscience  
In cases where the FDA-cleared segmentation was not interpretable due to improper definitions of the arterial input function, the decision forest provided reliable results; (4) Conclusions: The prediction  ...  Final infarct volume was determined on a routine follow-up MRI or CT at 90 days after the stroke; (3) Results: Compared to the reference standard (infarct volume after 90 days), the decision forest algorithm  ...  The segmentation was performed on a slice-by-slice basis in regions with a Tmax delay of >6 s to calculate the penumbra volume. These constraints were defined as ground truth for penumbra.  ... 
doi:10.3390/ctn5030021 fatcat:6r3ufnvqpzhf3ilhexlkkdd5ka

Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients

Anthony J. Winder, Susanne Siemonsen, Fabian Flottmann, Götz Thomalla, Jens Fiehler, Nils D. Forkert
2019 Scientific Reports  
This study considers random decision forest, generalized linear model, and k-nearest-neighbor machine learning classifiers in conjunction with three data normalization approaches (non-normalized, relative  ...  This study gives comprehensive consideration to these factors by comparing the agreement of voxel-based tissue outcome predictions using acute imaging and clinical parameters with manual lesion segmentations  ...  Acknowledgements The authors would like to thank the Heart and Stroke Foundation of Canada for Grant in aid support for this study.  ... 
doi:10.1038/s41598-019-49460-y pmid:31519923 pmcid:PMC6744509 fatcat:pcq5x47hhzhybaarsreyo3anoq

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  
The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke  ...  Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21248507 pmid:34960599 pmcid:PMC8707263 fatcat:zc4gtjhkoje2jotcqr5gvlatu4

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Oskar Maier, Bjoern H. Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich, Matthias Liebrand, Stefan Winzeck, Abdul Basit, Paul Bentley, Liang Chen, Daan Christiaens, Francis Dutil (+37 others)
2017 Medical Image Analysis  
We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference.  ...  A total of 16 research groups participated with a wide range of state-ofthe-art automatic segmentation algorithms.  ...  Thresholding is clearly not a suitable approach for penumbra estimation.  ... 
doi:10.1016/ pmid:27475911 pmcid:PMC5099118 fatcat:mmmolbl4dzbbzibtjh7nmot6hm

Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations

Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F. Aulmann, Amir Madany Mamlouk, Mattias P. Heinrich
2018 Frontiers in Neurology  
on acute perfusion data, yielding a Dice score overlap of 0.46 for predictions from expert segmentations of core and penumbra.  ...  The predictions are physiologically constrained to a shape embedding that encodes a continuous progression between the core and penumbra extents.  ...  ACKNOWLEDGMENTS We would like to thank Nvidia Corporation for providing us with a Titan Xp graphics card.  ... 
doi:10.3389/fneur.2018.00989 pmid:30534108 pmcid:PMC6275324 fatcat:htgdlkeqajhobliepeb5tlcota

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  
The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability.  ...  The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (  ...  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

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke

Y Yu, Y Xie, T Thamm, E Gong, J Ouyang, S Christensen, M P Marks, M G Lansberg, G W Albers, G Zaharchuk
2021 American Journal of Neuroradiology  
We explored 3 approaches: 1) separate: train 2 independent models with patients with minimal and major reperfusion; 2) pretraining: develop a single model using patients with partial and unknown reperfusion  ...  Deep learning models with fine-tuning lead to better performance for predicting tissue at risk and ischemic core, outperforming conventional thresholding methods.  ...  ACKNOWLEDGMENTS We appreciate the statistical consultation provided by Jarrett Rosenberg, PhD, and Tie Liang, EdD, Radiologic Sciences Laboratory, Department of Radiology, Stanford University.  ... 
doi:10.3174/ajnr.a7081 pmid:33766823 pmcid:PMC8191664 fatcat:t4utbhndwbhv3avqyoopl4tmzy

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Anthony Winder, Matthias Wilms, Jens Fiehler, Nils D. Forkert
2021 Biomedicines  
This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size.  ...  Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy  ...  These voxel-level imaging parameters have historically been used to determine tissue viability using a threshold-based approach that segments the affected tissue into three compartments: (1) the ischemic  ... 
doi:10.3390/biomedicines9101357 pmid:34680474 fatcat:grds7l2egfayzbix7wx62pnsvu

Artificial Intelligence and Acute Stroke Imaging

J.E. Soun, D.S. Chow, M. Nagamine, R.S. Takhtawala, C.G. Filippi, W. Yu, P.D. Chang
2020 American Journal of Neuroradiology  
Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes.  ...  Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta  ...  ACKNOWLEDGMENTS The authors thank Aidoc, Avicenna, Brainomix, RapidAI, and for providing information regarding commercially available products and sample images of their applications for publication  ... 
doi:10.3174/ajnr.a6883 pmid:33243898 pmcid:PMC7814792 fatcat:uwvjtq42ejfzjoi46eqlublmya

Machine learning and acute stroke imaging

Sunil A Sheth, Luca Giancardo, Marco Colasurdo, Visish M Srinivasan, Arash Niktabe, Peter Kan
2022 Journal of NeuroInterventional Surgery  
Additional applications and further integration with clinical care is inevitable. Thus, facility with these approaches is critical for the neurointerventional clinician.  ...  Imaging inputs have included non-contrast head CT, CT angiograph and MRI, with a range of performances.  ...  For brain imaging application, the brain is isolated using segmentation algorithms. 3. Registration. The image is aligned to a common space, either with rigid or non-rigid approaches.  ... 
doi:10.1136/neurintsurg-2021-018142 pmid:35613840 fatcat:rm5zfnd2vzdx5lekjnjmah7cza

Diffusion-Weighted Imaging-Alone Endovascular Thrombectomy Triage in Acute Stroke: Simulating Diffusion-Perfusion Mismatch Using Machine Learning

Yoon-Chul Kim, Woo-Keun Seo, In-Young Baek, Ji-Eun Lee, Ha-Na Song, Jong-Won Chung, Chi Kyung Kim, Kyungmi Oh, Sang-il Suh, Oh Young Bang, Gyeong-Moon Kim, Jeffrey L. Saver (+1 others)
2022 Journal of Stroke  
First, the diffusion-weighted imaging (DWI) lesion was automatically detected and segmented using a trained U-Net model.  ...  Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) in magnetic resonance imaging (MRI) are used to estimate ischemic core and penumbra volumes in patients with acute ischemic stroke.  ...  Inner_ADCr_interval 15 Volume in each ADC ratio interval in the inner brain region: (0.00-0.06), (0.06-0.12), (0.12-0.18), (0.18-0.24), (0.24-0.30), (0.30-0.36), (0.36-0.42), (0.42-0.48), (0.48-0.54),  ... 
doi:10.5853/jos.2021.02817 pmid:35135068 pmcid:PMC8829488 fatcat:xbjouq5hkvgdnmrex75xmn6ole

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

B. Jiang, G. Zhu, Y. Xie, J.J. Heit, H. Chen, Y. Li, V. Ding, A. Eskandari, P. Michel, G. Zaharchuk, M. Wintermark
2021 American Journal of Neuroradiology  
A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients.  ...  The model with the best-performing features had a better performance than the other machine learning models.  ...  Automatic segmentation of ischemic core and penumbra volumes was performed on the basis of previously published thresholds. 22 The sidedness of cerebral ischemia was evaluated as well.  ... 
doi:10.3174/ajnr.a6918 pmid:33414230 pmcid:PMC7872172 fatcat:vwysuuxp65er5pepzjjtfd4bjm

Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

Srinivasa Rao Kundeti, Manikanda Krishnan Vaidyanathan, Bharath Shivashankar, Sankar Prasad Gorthi
2021 BMJ Open  
We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration  ...  as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data.  ...  Similarly, in MRP cases, a threshold of <4-6 s is applied to the Tmax map to compute the penumbra volume.  ... 
doi:10.1136/bmjopen-2020-043665 pmid:33692180 fatcat:ij72c5yuqzcvhpbn4zhkhb5mhm

MR Images, Brain Lesions, and Deep Learning

Darwin Castillo, Vasudevan Lakshminarayanan, María José Rodríguez-Álvarez
2021 Applied Sciences  
segmentation of ischemic and demyelinating lesions.  ...  For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11041675 fatcat:ivwi2tp52ngstdoelbsjglhem4
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