Deep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage.

Introduction: Radiological scores used to assess the extent of subarachnoid hemorrhage are limited by intrarater and interrater variability and do not utilize all available information from the imaging. Image segmentation enables precise identification and delineation of objects or regions of interest and offers the potential for automatization of score assessments using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aneurysmal subarachnoid hemorrhage outcome prediction. Methods: A set of 73 non-contrast CT scans of patients with aneurysmal subarachnoid hemorrhage were included. Six target classes were manually segmented to create a multiclass segmentation ground truth: subarachnoid, intraventricular, intracerebral and subdural hemorrhage, aneurysms and ventricles. We used the 2d and 3d configurations of the nnU-Net deep learning biomedical image segmentation framework. Additionally, we performed an interrater reliability analysis in our internal test set ( Results: The nnU-Net-based segmentation model demonstrated performance closely matching the interrater reliability between two senior raters for the subarachnoid hemorrhage, ventricles, intracerebral hemorrhage classes and overall hemorrhage segmentation. For the hemorrhage segmentation a median Dice coefficient of 0.664 was achieved by the 3d model (0.673 = 2d model). In the external test set a median Dice coefficient of 0.831 for the hemorrhage segmentation was achieved. Conclusion: Deep learning enables automated multiclass segmentation of aneurysmal subarachnoid hemorrhage-related pathologies and achieves performance approaching that of a human rater. This enables automatized volumetries of pathologies identified on admission CTs in patients with subarachnoid hemorrhage potentially leading to imaging biomarkers for improved outcome prediction.