ArticleJournal of medical imaging (Bellingham, Wash.)2019
Active learning strategy and hybrid training for infarct segmentation on diffusion MRI with a U-shaped network.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 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
- Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.Journal of imaging informatics in medicine · 2025Article
- Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.Journal of imaging informatics in medicine · 2025Article
- Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets.Journal of imaging informatics in medicine · 2024Article
- Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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Authors and funding
8 authors.
Funding
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Abstract
Automatic and reliable stroke lesion segmentation from diffusion magnetic resonance imaging (MRI) is critical for patient care. Methods using neural networks have been developed, but the rate of false positives limits their use in clinical practice. A training strategy applied to three-dimensional deconvolutional neural networks for stroke lesion segmentation on diffusion MRI was proposed. Infarcts were segmented by experts on diffusion MRI for 929 patients. We divided each database as follows: 60% for a training set, 20% for validation, and 20% for testing. Our hypothesis was a two-phase hybrid learning scheme, in which the network was first trained with whole MRI (regular phase) and then, in a second phase (hybrid phase), alternately with whole MRI and patches. Patches were actively selected from the discrepancy between expert and model segmentation at the beginning of each batch. On the test population, the performances after the regular and hybrid phases were compared. A statistically significant Dice improvement with hybrid training compared with regular training was demonstrated (
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