Automated segmentation of compartmental hematoma volumes in patients with aneurysmal subarachnoid hemorrhage and its predictive capability for patient outcomes

Subarachnoid hemorrhage (SAH), a type of stroke that primarily affects younger individuals, is characterized by high mortality rates and the potential for long-term disabilities. This project focuses on aneurysmal subarachnoid hemorrhage (aSAH), where secondary brain injury (SBI) often develops days after the initial bleeding, significantly contributing to mortality and long-term morbidity. Its delayed onset makes it a potential disease modifier. Therefore, there is an unmet need to enhance the understanding about the impact of blood volume, it’s distribution and resolution on the occurrence of secondary brain-injury. The objective of this master’s thesis was to create a computer tomography (CT)-based automated volumetry tool that could identify the initial blood volume in different anatomical regions of the brain. Additionally, the tool aimed to predict post-SAH outcomes and the likelihood of secondary brain injury by tracking blood volume changes over time.