CCNI | Clinical Computational Neuroimaging Group

Deep learning model for segmenting acute stroke lesions publication selected as AJNR’s editorial choice

Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This also has important clinical implications as acute DWI infarct volumes are increasingly being used for triage of stroke patients presenting for late window interventions. A group of researchers from our department conducted a study to investigate whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps.

You can read our paper, “Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI,” here: The American Journal of Neuroradiology selected the study as Editor’s Choice in this month’s issue, which you can find here:

Originally published by MGH Rad Times: