A Novel Segmentation Approach Utilizing Object Detection Techniques as Prompts for a Zero-Shot System in Hemorrhagic Stroke Segmentation in CT Images

Stroke is a leading cause of death globally, with higher chances of recovery when prompt and accurate diagnosis is followed by appropriate treatment. Various neuroimaging techniques, including computed tomography (CT), are used for stroke detection. Computer-aided diagnosis (CAD) systems can capture information imperceptible to the human eye, making them valuable tools in stroke diagnosis. This study proposes a novel approach for segmenting hemorrhagic stroke in CT scans using Deep Learning. Specifically, we evaluate the effectiveness of SSD, YOLO-v4, and YOLACT as prompts for the Segment Anything Model (SAM) in hemorrhagic stroke segmentation. Additionally, we compare YOLACT and SAM for segmentation performance. The methods showed promising results, with the proposed SAM and MobileSAM achieving an accuracy of 99.82%, while YOLACT attained 99.74%. The use of zeroshot and one-stage models demonstrated exceptional efficiency in addressing the segmentation challenges in medical images.