Segmentation of Acute Ischemic Stroke in Native and Enhanced CT using Uncertainty-aware Labels

In stroke diagnosis, a non-contrast CT (NCCT) is the first scan acquired and bears the possibility to identify ischemic changes in the brain. Their identification and segmentation are subject to high inter-rater variability. We develop and evaluate models based on labels that reflect the uncertainty in segmentation hypotheses by annotation of minimum (“inner”) and maximum (“outer”) contours of perceived presence of infarct core and hypoperfused tissue. These labels are used for training nnU-Net to segment both from NCCT and CT angiography (CTA) scans of 167 patients. The predicted output is post-processed to obtain delineations of the tissue of interest at varying distances between inner and outer contours. Compared to the ground truth, infarcts of medium size (10 to 70 ml) could be segmented in the NCCT scans with a median error of 3.7 ml (6.2 ml for CTA) of excess predicted volume, missing 6.4 ml (3.5 ml) of the infarct.