Ischemic Stroke Lesion Segmentation Using Multi-Plane Information Fusion
نویسندگان
چکیده
منابع مشابه
Ischemic Stroke Lesion Segmentation
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. An Expectation Maximizationapproach is used for estimating intensity models for both normal and pathological tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as either nor...
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This work proposes fully automatic ischemic stroke lesion segmentation in multimodality brain MRI by extending our prior brain tumor segmentation (BTS) work [1]. The extensions of the BTS method include development of relevant MR image intensity inhomogeneity correction, several new features and feature ranking methods. We characterized brain lesions with multiple features such as piece-wise tr...
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This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to ...
متن کاملCorrection: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
There is an error in the Conclusions section of the manuscript. The entire Conclusions section was not included. The Conclusions section should read: In this work, nine different classifiers were used for ischemic stroke lesion segmentation from brain MRI images and evaluated using different ground truth sets and scenarios. Based on the results of this study, it seems justified to recommend RDF...
متن کاملClassifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study.
MOTIVATION Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult t...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2977415