Key result
Multiparametric ensemble neural networks segment acute myocardial infarcts more accurately than single-map models.
p-value: p=<.001
BACKGROUND AND PURPOSE: 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 study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. MATERIALS AND METHODS: ). CONCLUSIONS: Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.
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Winzeck et al. (2019) studied Acute ischemic stroke. Ensemble of convolutional neural networks trained on multiparametric DWI maps vs. Single networks trained on solo DWI parametric maps was evaluated on Automated infarct segmentation accuracy (p=<.001). Ensembles of convolutional neural networks trained with multiparametric maps segmented acute infarcts more accurately than a single model trained with a solo map (P<.001).
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