Abstract WMP17: Fully-Automated Ischemic Brain Infarct Volumetric Segmentation in Diffusion Weighted MR using Deep Learning

Abstract

Introduction: Brain imaging is a key step in the clinical evaluation of ischemic stroke, with Diffusion Weighted Magnetic Resonance (DWI) being a key imaging modality, as it allows for assessment of extent of acute ischemic brain injury. Manual delineation of stroke regions is expensive, time-consuming, and subject to inter-rater variability. In this study, we develop a deep learning approach for ischemic stroke volumetric segmentation in a large clinical dataset of 1,239 patients from the NIH-funded Heart-Brain Interactions in Human Acute Ischemic Stroke Study utilizing only DWI imaging. Hypothesis: Deep learning can be used to automatically calculate stroke volumes in high agreement with manual human expert segmentations. Methods: The patients were randomly divided into Training (n = 743), Validation (n=248), and Testing (n=248). We implemented the 3D U-Net neural network architecture. Additionally, we modified the 3D U-Net by incorporating incorporate state-of-the-art components that have improved neural network architectures for classification tasks, namely residual connections, inception modules, dense connections, and squeeze-and-excitation modules. Results: The best performing individual model was the Inception U-Net, which had a median dice similarity coefficient of 0.720 (0.011-0.920) within the Testing Set. In comparing manually and automatically derived infarct volumes, the Intraclass Correlation Coefficient was 0.974 (p<.0001) in the Testing Set. Conclusions: Our fully-automatic pipeline for stroke segmentation demonstrates the potential for deep learning-based tools to automate ischemic stroke volumetrics.