2088 An Automated Method for Image Segmentation, Stereotactic Localization, and Functional Outcome Prediction of Basal Ganglia Hemorrhages

INTRODUCTION: Automated software platforms offer intracranial hemorrhage volume calculation, but do not provide functional outcome analytics. In a prior study (Abstract 44144), we demonstrated that stereotaxy-enabled basal ganglia intracranial hemorrhage (bgICH) spatial representation can predict functional outcome using manually segmented bgICH volumes and an anatomic coordinate frame in surgically managed patients. However, manual segmentation and landmark identification is cumbersome and is unlikely to be clinically adopted. METHODS: A training set of 678 CTH and CTA (computed tomography head and angiography) images from 63 patients were used to train key-point detection and instance segmentation convolutional neural network-based models for anatomic landmark identification and bgICH segmentation. Anatomic landmarks included the bilateral orbital rims at the globe’s maximum diameter and the posterior-most aspect of the tentorial incisura. The models were tested using volumetric CTH/CTA scans from 42 patients who underwent MIS bgICH evacuation with recorded modified Rankin Scales (mRS) within one year following surgery. RESULTS: bgICH volumes were highly correlated (R2 = 0.95, p<0.001) and statistically similar between manual (median 40-mL) and automatic (median 39-mL) segmentation methods (p=0.677). The absolute median difference between groups was 2-mL (IQR: 1-6 mL). The median intersection-over-union between both volumes was excellent (0.81, IQR: 0.74-0.84). Median localization accuracy (distance between automated and manually designated coordinate frames) was 4-mm (IQR: 3-6). Landmark coordinates were highly correlated in the x- (left-right), y- (up-down), and z-axes (axial CT-slice) for all three landmarks (R2 range = 0.95-0.99, p<0.001 for all). Functional outcome (mRS 4-6) was predicted with similar model performance using automated (AUC = 0.81) and manually (AUC = 0.84) constructed spatial representation models (p=0.173). CONCLUSIONS: Computer vision models can accurately replicate bgICH manual segmentation, stereotactic localization, and prognosticate functional outcomes following MIS bgICH evacuation.