Abstract TMP84: Multi-Institutional Validation of an Automated Segmentation Tool for Intracerebral Hemorrhage (ICH) and Peri-Hematomal Edema (PHE) on Head CT

Abstract

Objective: In patients with acute intracerebral hemorrhage (ICH), the volume of hemorrhage, and perihematomal edema (PHE) are representative of primary, and secondary brain injury, respectively. Automated quantification of ICH and PHE volumes from admission non-contrast head CT can facilitate evaluation of large stroke datasets, and expedite treatment triage when volume cutoffs are applied. We aimed to train and externally validate an automated model for segmentation and quantification of ICH and PHE volumes on non-contrast head CT scans. Methods: For training of the model, we used the data from multicentric ATACH-2 clinical trial with head CTs from eleven medical institutes in six countries. The model was then exported and tested on two external datasets from Yale and University of Berlin. We designed an automated pipeline for extracting brain window from 3D non-contrast head CT, skull stripping, resampling to homogenous voxel size, and segmentation. For segmentation we applied a deep learning model based on 3D full resolution nnUNet. We used the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Volume Similarity (VS) between automated segmentation and ground truth manual segmentation volumes to evaluate the models’ performance. Results: A total of 854 patients from the ATACH-2 trial (854 х2, baseline and follow-up CT scans) were used for training of the model in 5-fold cross-validation. In validation folds, the model achieved a mean DSC=0.90±0.10, HD =1.42±16.0mm, and VS=0.95±0.10 for ICH; and DSC=0.74±0.12, HD =3.0±16.5 mm, and VS=0.91±0.12 for PHE. In the externaltesting cohort from Yale (200 patients х2, baseline and follow-up CT scans), the model archieved median DSC=0.93, HD=0.98 mm, and VS=0.97 for ICH; and median DSC=0.69, HD=4.52 mm, and VS=0.84 for PHE. In the externaltesting cohort from Charité Hospital, University of Berlin (915 baseline CT scans), the model archived median DSC=0.87, HD=4 mm, and VS=0.90 for ICH; and median DSC=0.65, HD=5 mm, VS=0.85 for PHE. Conclusion: We trained, validated, and externally tested an end-to-end automated tool for segmentation of both ICH and PHE on non-contrast head CT. Such tool can facilitate treatment triage for trials when volume cutoffs are applied for enrollment.