Abstract 97: Development And Validation Of A Deep Machine Learning Tool For Automated Intraventricular Hemorrhage Segmentation And Volume Measurement Using 3d Convolutional Neural Networks

Abstract

Introduction: Development of new intraventricular hemorrhage (IVH) within 24 hours of presentation or expansion of 1mL or more independently predicts poor outcome. Current methods for IVH volume estimation lack sufficient precision to detect this level of change. The best method of quantification with manual/semi-automated segmentation remain clinically impractical. We aimed to train and validate a convolutional neural network (CNN) to autonomously segment and quantify IVH volume in acute intracerebral hemorrhage (ICH) patients. Methods: Imaging data for training and validation was obtained from 3 international multicenter studies. IVH volumes were assessed using CNN, IVH Score (IVHS), Original Graeb Score (oGS), Modified Graeb Score (mGS), and manual segmentation using ITK-SNAP. Agreement between CNN volume and IVHS estimated volume compared to manual segmentation was assessed using intraclass correlation coefficient (ICC) and Bland-Altman charts. Accuracy of CNN segmentation was compared to manual segmentation with Dice similarity coefficient (DSC). Accuracy of hematoma expansion detection was assessed using receiver operating characteristic curves. Results: A total of 172 patients and 311 CT scans were included. Intra-rater reliability was significantly greater for CNN versus IVHS method (ICC 0.99 [95%CI 0.99 - 1.00] vs 0.76 [95%CI 0.66 - 0.83]). Accuracy of CNN segmentation was satisfactory (DSC 0.76 [95%CI 0.75 - 0.78]). Accuracy of hematoma expansion detection was substantially greater in CNN (AUC 0.91 [95%CI 0.85 - 0.97]) versus IVHS, oGS and mGS. Conclusion: Our results demonstrate that a fully automated CNN algorithm is capable of segmenting IVH volumes on multi-center data with higher intra-rater reliability and satisfactory accuracy over the current methods of IVH volume measurement tools. The algorithm has proven to be capable of detecting hematoma expansion with substantially greater accuracy over existing methods of IVH measurement.