Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

Abstract

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.

Fig 1.

Median Dice (80.2% [IQR, 56.6%–88.9%]), precision (82.9% [IQR, 59.7%–92.2%]), and sensitivity (86.2% [IQR, 71.1%–92.3%]) scores of the DWI+ADC+LOWB ensemble on the Evaluation Cohort. The white bar within the violin plot shows the IQR, mean is a diamond, and median is an X.

Fig 2.

Sample segmentation results of the ensemble of DWI+ADC+LOWB (blue regions) on sample subjects along with manual outlines (red outlines). A, A small lesion example from a 70-year-old man with an admission NIHSS score of 1, imaged approximately 9 hours from LKW: MLV = 0.96 cm³, ALV = 1.07 cm³, Dice = 89.4%. B, Medium lesion sample from a 38-year-old woman with an admission NIHSS score of 4, imaged approximately 10 hours from LKW: MLV = 54.3 cm³, ALV = 57.9 cm³, Dice = 95.7%. C, A large lesion example from a 62-year-old man with an undocumented admission NIHSS score, imaged approximately 10 hours from LKW: MLV = 229.0 cm³, ALV = 208.7 cm³, Dice = 94.0%.