ArticleFrontiers in neuroinformatics2019
Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).Frontiers in neurology · 2025Pooled it
- Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2022Pooled it
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity.Communications medicine · 2024Article
- Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity.Research square · 2023Article
- Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network.NeuroImage. Clinical · 2023Article
- Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography.International journal of computer assisted radiology and surgery · 2022Article
- A Review on Computer Aided Diagnosis of Acute Brain Stroke.Sensors (Basel, Switzerland) · 2021Review
- Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.Radiological physics and technology · 2021Article
- Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.Physics in medicine and biology · 2020Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood's passage through the brain's vascular network. Therefore, it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions.
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