Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images

TL;DR

This paper introduces a deep learning-based automated method for detecting the hyperdense MCA sign on non-contrast CT scans to improve early stroke diagnosis and reduce treatment delays.

Contribution

It presents a novel automated segmentation approach for the MCA dot sign using deep learning, addressing inter-observer variability in stroke diagnosis.

Findings

Effective detection of MCA dot sign demonstrated

Reduces diagnosis time and observer variability

Potential to improve stroke treatment outcomes

Abstract

The hyperdense middle cerebral artery (MCA) dot sign has been reported as an important factor in the diagnosis of acute ischemic stroke due to large vessel occlusion. Interpreting the initial CT brain scan in these patients requires high level of expertise, and has high inter-observer variability. An automated computerized interpretation of the urgent CT brain image, with an emphasis to pick up early signs of ischemic stroke will facilitate early patient diagnosis, triage, and shorten the door-to-revascularization time for these group of patients. In this paper, we present an automated detection method of segmenting the MCA dot sign on non-contrast CT brain image scans based on powerful deep learning technique.