New to MyHealth?
Manage Your Care From Anywhere.
Access your health information from any device with MyHealth. You can message your clinic, view lab results, schedule an appointment, and pay your bill.
Purpose: To test the efficacy of lesion segmentation using a deep learning algorithm on non-contrast material-enhanced CT (NCCT) images with synthetic lesions resembling acute infarcts.Materials and Methods: In this retrospective study, 40 diffusion-weighted imaging (DWI) lesions in patients with acute stroke (median age, 69 years; range, 62-76 years; 17 women; screened between 2011 and 2017) were coregistered to 40 normal NCCT scans (median age, 70 years; range, 55-76 years; 25 women; screened between 2008 and 2011), which produced 640 combinations of DWI-NCCT with and without lesions for training (n = 420), validation (n = 110), and testing (n = 110). The signal intensity on the NCCT scans was depressed by 4 HU (a 13% drop) in the region of the diffusion-weighted lesion. Two U-Net architectures (standard and symmetry aware) were trained with two different training strategies. One was a naive strategy, in which the model started training with random coefficients. The other was a progressive strategy, which started with coefficients derived from a model trained on a dataset with lesions that were depressed by 10 HU. The Dice scores from the two architectures and training strategies were compared from the test dataset.Results: Dice scores of symmetry-aware U-Nets were 25% higher than those of standard U-Nets (median, 0.49 vs 0.65; P < .001). Use of a progressive training strategy had no clear effect on model performance.Conclusion: Symmetry-aware U-Nets offer promise for segmentation of acute stroke lesions on NCCT scans.Keywords: Adults, CT-Quantitative, StrokeSupplemental material is available for this article.©RSNA, 2021.
View details for DOI 10.1148/ryai.2021200127
View details for PubMedID 34350404