1. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI
- Author
-
A. G. Sorensen, William A. Copen, Aneesh B. Singhal, Pamela W. Schaefer, Stefan Winzeck, Izzuddin Diwan, Hakan Ay, Steven J. T. Mocking, Ben Glocker, Priya Garg, Konstantinos Kamnitsas, Ona Wu, Elissa C. McIntosh, W. T Kimberly, Raquel Bezerra, Aurauma Chutinet, and Mark J. R. J. Bouts
- Subjects
Male ,Automated segmentation ,Neuroimaging ,Convolutional neural network ,Article ,Brain Ischemia ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Segmentation ,cardiovascular diseases ,Acute ischemic stroke ,Aged ,Parametric statistics ,Single model ,Artificial neural network ,business.industry ,Pattern recognition ,Middle Aged ,Stroke ,Diffusion Magnetic Resonance Imaging ,Female ,Neural Networks, Computer ,Neurology (clinical) ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Diffusion 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 investigates whether an ensemble of convolutional neural networks (CNN) trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. MATERIALS AND METHODS: CNNs were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by Dice score, sensitivity and precision) were compared to one another and to ensembles of 5 networks. To assess the generalizability of the approach, the best performing model was applied to an independent evaluation cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesions volumes was calculated across multiple thresholds (21 cm(3), 31 cm(3), 51 cm(3), and 70 cm(3)). RESULTS: An ensemble of CNNs trained on DWI, ADC and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (p
- Published
- 2019
- Full Text
- View/download PDF