Abstract
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. Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm3). An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001). 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.
Highlights
MethodsConvolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects
BACKGROUND AND PURPOSEAccurate 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
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
Summary
Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. MR imaging from patients with acute ischemic stroke admitted at a single academic medical center between 2005 and 2007, imaged within 12 hours of when the patient was last known to be well (LKW), and who did not receive either thrombolysis before MR imaging or experimental therapy were used for training the convolutional neural networks (CNNs).[11] An independent cohort[12,13] consisting of nonoverlapping patients admitted to the same center between 1996 and 2012 for whom imaging was performed within 24 hours of LKW and for whom follow-up MR imaging datasets were available was used for the evaluation group Both cohorts were drawn from separate repositories for which manual outlines were available that had been drawn several years ago for a study of early-stage stroke patterns[11] or for studies predicting lesion expansion.[12,13]. We randomly selected 10 subjects from the Evaluation Cohort and outlines drawn by reader 1, and 2-way intraclass correlation was calculated
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