LAUSR

Abstract. Purpose The size and location of infarct and penumbra are key to decision-making for acute ischemic stroke (AIS) management. CT perfusion (CTP) software estimate infarct and penumbra volume using contralateral hemisphere relative thresholding. This approach is not robust and widely contested by the scientific community. In this study, we investigate the use of deep learning-based algorithms to efficiently locate infarct and penumbra tissue on CTP hemodynamic maps. Approach CTP scans were retrospectively collected for 60 and 59 patients in the infarct only and infarct + penumbra substudies respectively. Commercial CTP software was used to generate cerebral blood flow, cerebral blood volume, mean transit time, time to peak, and delay time maps. U-Net-shaped architectures were trained to segment infarct or infarct + penumbra. Test-time-augmentation, ensembling, and watershed segmentation were used as postprocessing techniques. Segmentation performance was evaluated using Dice coefficients (DC) and mean absolute volume errors (MAVE). Results The algorithm segmented infarct tissue resulted in DC of 0.64  ±  0.03 (0.63, 0.65), and MAVE of 4.91  ±  0.94 (4.5, 5.32) mL. In comparison, the commercial software predicted infarct with a DC of 0.31  ±  0.17 (0.26, 0.36) and MAVE of 9.77  ±  8.35 (7.12, 12.42) mL. The algorithm was able to segment infarct + penumbra with a DC of 0.61  ±  0.04 (0.6, 0.63), and MAVE of 6.51  ±  1.37 (5.91, 7.11) mL. In comparison, the commercial software predicted infarct + penumbra with a DC of 0.3  ±  0.19 (0.25, 0.35) and MAVE of 9.18  ±  7.55 (7.25, 11.11) mL. Conclusions Use of deep learning algorithms to assess severity of AIS in terms of infarct and penumbra volume is precise and outperforms current relative thresholding methods. Such an algorithm would enhance the reliability of CTP in guiding treatment decisions.