RT Journal Article
SR Electronic
T1 Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A6077
A1 S. Winzeck
A1 S.J.T. Mocking
A1 R. Bezerra
A1 M.J.R.J. Bouts
A1 E.C. McIntosh
A1 I. Diwan
A1 P. Garg
A1 A. Chutinet
A1 W.T. Kimberly
A1 W.A. Copen
A1 P.W. Schaefer
A1 H. Ay
A1 A.B. Singhal
A1 K. Kamnitsas
A1 B. Glocker
A1 A.G. Sorensen
A1 O. Wu
YR 2019
UL http://www.ajnr.org/content/early/2019/05/30/ajnr.A6077.abstract
AB 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: 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).RESULTS: 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).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.ALVautomatically segmented lesion volumeCNNconvolutional neural networkE2ensemble of CNNs using DWI and ADCE3ensemble of CNNs using DWI, ADC, and LOWBIQRinterquartile rangeLKWlast known to be wellLOWBlow b-value diffusion-weighted image (b0)MLVmanually segmented lesion volume