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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  ...  Used abbreviations are: white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), random forest (RF), extremely randomized trees (ET), contextual clustering (CC), gaussian mixture models (GMM),  ... 
doi:10.1016/j.media.2016.07.009 pmid:27475911 pmcid:PMC5099118 fatcat:mmmolbl4dzbbzibtjh7nmot6hm

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

Deep Learning Trends for Focal Brain Pathology Segmentation in MRI [chapter]

Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin
2016 Lecture Notes in Computer Science  
Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease  ...  Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning.  ...  ISLES benchmark Ischemic Stroke Lesion Segmentation (ISLES) challenge started in 2015 and is held in conjunction with the Brain Lesion workshop as part of MICCAI.  ... 
doi:10.1007/978-3-319-50478-0_6 fatcat:vuheit2riffn3aun5u4rchgmgi

Convolutional neural networks in medical image understanding: a survey

D. R. Sarvamangala, Raghavendra V. Kulkarni
2021 Evolutionary Intelligence  
The major medical image understanding tasks, namely image classification, segmentation, localization and detection have been introduced.  ...  Imaging techniques are used to capture anomalies of the human body. The captured images must be understood for diagnosis, prognosis and treatment planning of the anomalies.  ...  Acknowledgements The authors acknowledge with gratitude the support received from REVA University, Bengaluru, and M. S. Ramaiah University of Applied Sciences, Bengaluru, India.  ... 
doi:10.1007/s12065-020-00540-3 pmid:33425040 pmcid:PMC7778711 fatcat:ykdwhdv3pzfqpnueyieinxofie

Advancing efficiency and robustness of neural networks for imaging

Konstantinos Kamnitsas, Benjamin Glocker, Daniel Rueckert
2020
Of particular interest is the application of segmenting volumetric medical scans because of the technical challenges it imposes, as well as its clinical importance.  ...  It investigates domain adaptation and introduces an architecture for adversarial networks tailored for adaptation of segmentation networks.  ...  Additionally, our pipeline achieved state-of-the-art performance on both public benchmarks of brain tumors (BRATS 2015) and stroke lesions (SISS ISLES 2015) .  ... 
doi:10.25560/80157 fatcat:mv3q2zargfamrifgqwfycd53mq