ArticleBMC medical informatics and decision making2023
CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Quantitative generation of diffusion-weighted imaging from non-contrast CT in acute ischemic stroke: a multi-scanner deep-learning model with external validation.Quantitative imaging in medicine and surgery · 2026Article
- Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Mechanisms and integrative machine learning approaches to blood-brain barrier biomarker profiling for personalized ischemic stroke management.Physiological reports · 2026Review
- Impact of simulated glasses noise on facial emotion recognition with deep learning models.Scientific reports · 2026Article
- Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.Journal of imaging informatics in medicine · 2026Article
- Automated Multi-Modal MRI Segmentation of Stroke Lesions and Corticospinal Tract Integrity for Functional Outcome Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Electricity price forecasting with ensemble meta-models and SHAP explainers: a PCA-driven approach.Scientific reports · 2026Article
- Parameter-free dual-student ensemble for semi-supervised ischemic stroke segmentation on follow-up NCCT.Frontiers in neurology · 2026Article
- Robustness analysis of YOLO and faster R-CNN for object detection in realistic weather scenarios with noise augmentation.Scientific reports · 2025Article
- Artificial intelligence and stroke imaging.Current opinion in neurology · 2025Review
- Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning.International journal of radiation oncology, biology, physics · 2025Article
- Prediction of prognosis in acute ischemic stroke after mechanical thrombectomy based on multimodal MRI radiomics and deep learning.Frontiers in neurology · 2025Article
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5 authors.
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Abstract
backgroundAccurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a novel deep convolutional neural network (CNN-Res) that automatically performs the segmentation of ischemic stroke lesions from multimodal MRIs.
methodsCNN-Res used a U-shaped structure, so the network has encryption and decryption paths. The residual units are embedded in the encoder path. In this model, to reduce gradient descent, the residual units were used, and to extract more complex information in images, multimodal MRI data were applied. In the link between the encryption and decryption subnets, the bottleneck strategy was used, which reduced the number of parameters and training time compared to similar research.
resultsCNN-Res was evaluated on two distinct datasets. First, it was examined on a dataset collected from the Neuroscience Center of Tabriz University of Medical Sciences, where the average Dice coefficient was equal to 85.43%. Then, to compare the efficiency and performance of the model with other similar works, CNN-Res was evaluated on the popular SPES 2015 competition dataset where the average Dice coefficient was 79.23%.
conclusionThis study presented a new and accurate method for the segmentation of MRI medical images using a deep convolutional neural network called CNN-Res, which directly predicts segment maps from raw input pixels.
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