Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images
This addresses the need for faster and more consistent diagnosis of acute ischemic stroke in patients, though it appears incremental as it applies deep learning to a specific medical imaging task.
The paper tackled the problem of detecting the hyperdense middle cerebral artery sign in acute ischemic stroke on non-contrast CT images, presenting an automated segmentation method using deep learning to facilitate early diagnosis and reduce door-to-revascularization time.
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.