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ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
2017
Medical Image Analysis
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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
2021
Sensors
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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]
2016
Lecture Notes in Computer Science
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1607.05258v2 fatcat:wlhnllsxcjbohmxrbri2xmhqaaOther Versions
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
2021
Evolutionary Intelligence
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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
2020
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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