Development of a Deep Learning Method to Identify Acute Ischemic Stroke
  Lesions on Brain CT
Alessandro Fontanella Wenwen Li Grant Mair Antreas Antoniou Eleanor Platt Paul Armitage Emanuele Trucco Joanna Wardlaw Amos Storkey
Computed Tomography (CT) is commonly used to image acute ischemic stroke
(AIS) patients, but its interpretation by radiologists is time-consuming and
subject to inter-observer variability. Deep learning (DL) techniques can
provide automated CT brain scan assessment, but usually require annotated
images. Aiming to develop a DL method for AIS using labelled but not annotated
CT brain scans from patients with AIS, we designed a convolutional neural
network-based DL algorithm using routinely-collected CT brain scans from the
Third International Stroke Trial (IST-3), which were not acquired using strict
research protocols. The DL model aimed to detect AIS lesions and classify the
side of the brain affected. We explored the impact of AIS lesion features,
background brain appearances, and timing on DL performance. From 5772 unique CT
scans of 2347 AIS patients (median age 82), 54% had visible AIS lesions
according to expert labelling. Our best-performing DL method achieved 72%
accuracy for lesion presence and side. Lesions that were larger (80% accuracy)
or multiple (87% accuracy for two lesions, 100% for three or more), were better
detected. Follow-up scans had 76% accuracy, while baseline scans 67% accuracy.
Chronic brain conditions reduced accuracy, particularly non-stroke lesions and
old stroke lesions (32% and 31% error rates respectively). DL methods can be
designed for AIS lesion detection on CT using the vast quantities of
routinely-collected CT brain scan data. Ultimately, this should lead to more
robust and widely-applicable methods.