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ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images [chapter]

Tom Haeck, Frederik Maes, Paul Suetens
2016 Lecture Notes in Computer Science  
We present a novel fully-automated generative ischemic stroke lesion segmentation method that can be applied to individual patient images without need for a training data set.  ...  The performance of the method for segmenting the ischemic stroke is summarized by an average Dice score of 0.78 and 0.51 for the SPES and SISS 2015 training set respectively.  ...  Introduction The MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge comprises the automatic segmentation of ischemic stroke lesions acquired in the sub-acute stroke development stage (SISS) and  ... 
doi:10.1007/978-3-319-30858-6_21 fatcat:frfomcsq5ndqdgztf4ks4hgwgy

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.  ...  In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke  ...  Setup of ISLES Image segmentation challenges aim at an independent and fair comparison of various segmentation methods for a given segmentation task.  ... 
doi:10.1016/j.media.2016.07.009 pmid:27475911 pmcid:PMC5099118 fatcat:mmmolbl4dzbbzibtjh7nmot6hm

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.  ...  A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016.  ...  Herein, we present an automated segmentation method for ischemic stroke lesion segmentation in multi-modal MRI images.  ... 
doi:10.3389/fneur.2018.00679 pmid:30271370 pmcid:PMC6146088 fatcat:fpjctcngobblpkzeror2fpsncu

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  ...  There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21248507 pmid:34960599 pmcid:PMC8707263 fatcat:zc4gtjhkoje2jotcqr5gvlatu4

Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network [article]

Zhiyang Liu, Chen Cao, Shuxue Ding, Tong Han, Hong Wu, Sheng Liu
2018 arXiv   pre-print
In this paper, we propose a deep learning method to automatically segment ischemic stroke lesions from multi-modal MR images.  ...  While the high quality medical resources are quite scarce across the globe, an automated diagnostic tool is expected in analyzing the magnetic resonance (MR) images to provide reference in clinical diagnosis  ...  In the sub-acute stroke lesion segmentation task of the ischemic stroke lesion segmentation (ISLES) 2015 challenge, the dataset is much smaller, with 28 patients in the training dataset and 36 patients  ... 
arXiv:1803.05848v1 fatcat:z4fp7yc4m5hnrgbavji5xcepu4

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks

Albert Clèrigues, Sergi Valverde, Jose Bernal, Jordi Freixenet, Arnau Oliver, Xavier Lladó
2019 Computers in Biology and Medicine  
For evaluation, the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing.  ...  In this work, we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images.  ...  The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this research.  ... 
doi:10.1016/j.compbiomed.2019.103487 pmid:31629272 fatcat:nddf664wqva6zcc7di6xgt4ffm

RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields

Gaoxiang Chen, Qun Li, Fuqian Shi, Islem Rekik, Li Wang, Zhifang Pan
2020 NeuroImage  
We evaluated the proposed method on the publicly available brain tumor datasets BRATS 2015 & BRATS 2018, as well as the ischemic stroke dataset ISLES 2015.  ...  However, due to noise, motion, and partial volume effects, automated segmentation of lesions from MRI is still a challenging task.  ...  Next, the sub-acute ischemic stroke lesion segmentation (SISS) dataset of the 2015 Ischemic Stroke Lesion Segmentation (ISLES) challenge at MICCAI was used to test ischemic stroke segmentation.  ... 
doi:10.1016/j.neuroimage.2020.116620 pmid:32057997 fatcat:hrpswp3ucncd3onnazqhmhdmra

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 task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis.  ...  We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model.  ...  Data As a dataset, we use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge), which consists of 3D medical CT images of the brain [30] .  ... 
doi:10.32604/cmc.2022.020998 fatcat:d2yr52qtvfhkfceyxkwvhugtsy

Acute and sub-acute stroke lesion segmentation from multimodal MRI [article]

Albert Clèrigues, Sergi Valverde, Jose Bernal, Jordi Freixenet, Arnau Oliver, Xavier Lladó
2019 arXiv   pre-print
The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015).  ...  Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each  ...  The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this research.  ... 
arXiv:1810.13304v2 fatcat:rpkxfq47kbbazigljfyffbv54q

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson
2017 Journal of digital imaging  
This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI.  ...  Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data.  ...  Acknowledgements This work was supported by National Institutes of  ... 
doi:10.1007/s10278-017-9983-4 pmid:28577131 pmcid:PMC5537095 fatcat:lekbdtmkx5cchmuutntacymrzu

MR Images, Brain Lesions, and Deep Learning

Darwin Castillo, Vasudevan Lakshminarayanan, María José Rodríguez-Álvarez
2021 Applied Sciences  
resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases.  ...  segmentation of ischemic and demyelinating lesions.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11041675 fatcat:ivwi2tp52ngstdoelbsjglhem4

Deep Learning for Medical Image Analysis [article]

Mina Rezaei, Haojin Yang, Christoph Meinel
2017 arXiv   pre-print
In this report, as an example, we explore different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation.  ...  This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning  ...  High and Low grade glioma(Tumor) 2 This data is from BRATS (Brain Tumor Segmentation) challenges in MICCAI conference 2015.  ... 
arXiv:1708.08987v1 fatcat:6i45mpd2vreyhf3nx7cnll2fl4

Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

S. Winzeck, S.J.T. Mocking, R. Bezerra, M.J.R.J. Bouts, E.C. McIntosh, I. Diwan, P. Garg, A. Chutinet, W.T. Kimberly, W.A. Copen, P.W. Schaefer, H. Ay (+5 others)
2019 American Journal of Neuroradiology  
Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects.  ...  This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps.  ...  ACKNOWLEDGMENTS We gratefully acknowledge the support of NVIDIA Corporation with the donation of the NVIDIA Tesla K40 GPU used for this research.  ... 
doi:10.3174/ajnr.a6077 pmid:31147354 pmcid:PMC6715290 fatcat:37g2v7qu3fbovk7txu74lozjsa

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Liang Chen, Paul Bentley, Daniel Rueckert
2017 NeuroImage: Clinical  
In this paper, we propose a novel framework to automatically segment stroke lesions in DWI.  ...  Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians.  ...  Acknowledgement This work is supported by the NIHR Grant i4i: Decision-assist for management of acute ischemic stroke using brain-imaging machinelearning (Ref: II-LA-0814-20007).  ... 
doi:10.1016/j.nicl.2017.06.016 pmid:28664034 pmcid:PMC5480013 fatcat:fgefw7bjifap7p5wmugu2b6pqa

Ischemic Stroke Lesion Segmentation Using MULTI-PLANE Information Fusion

Long Zhang, Ruoning Song, Yuanyuan Wang, Chuang Zhu, Jun Liu, Jie Yang, Lian Liu
2020 IEEE Access  
INDEX TERMS Ischemic stroke, DWI, feature fusion, image segmentation, deep learning.  ...  Diffusion-weighted magnetic resonance imaging (DWI) is sensitive to acute ischemic stroke and is a common diagnostic method for the stroke.  ...  The segmentation of sub-acute ischemic stroke lesion is one of the tasks in ISLES 2015, which attracts many entries.  ... 
doi:10.1109/access.2020.2977415 fatcat:gmddy735r5bzbhw7svp45gbsb4
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