Deep Learning-based Multiclass Segmentation in Aneurysmal Subarachnoid Hemorrhage

Abstract

Introduction Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with a significant variability in patients’ outcomes. Radiographic scores used to assess the extent of SAH or other potentially outcome-relevant pathologies are limited by interrater variability and do not utilize all available information from the imaging. Image segmentation plays an important role in extracting relevant information from images by enabling precise identification and delineation of objects or regions of interest. Thus, segmentation offers the potential for automatization of score assessments and downstream outcome prediction using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aSAH outcome prediction.