ArticleBioMed research international2014
Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted MR imaging and apparent diffusion coefficient map.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of novel technology-based interventions for ischemic stroke.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Pooled it
- Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.European radiology · 2022Article
- Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning.Biomedicines · 2022Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI.AJNR. American journal of neuroradiology · 2019Article
- Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI.Medical & biological engineering & computing · 2018Article
- ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.Medical image analysis · 2017Article
- Segmentation of Hyperacute Cerebral Infarcts Based on Sparse Representation of Diffusion Weighted Imaging.Computational and mathematical methods in medicine · 2016Article
- Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction.PloS one · 2014Article
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Authors and funding
12 authors.
Funding
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Abstract
Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.
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