半影
医学
分割
灌注
核医学
灌注扫描
梗塞
放射科
人工智能
心肌梗塞
心脏病学
缺血
计算机科学
作者
Mohammad Mahdi Shiraz Bhurwani,T. Boutelier,Adam J. Davis,Grégory Gautier,Dennis Swetz,Ryan A. Rava,Dorian Raguenes,Muhammad Waqas,Kenneth V. Snyder,Adnan H. Siddiqui,Ciprian N. Ionita
出处
期刊:Journal of medical imaging
[SPIE - International Society for Optical Engineering]
日期:2023-01-09
卷期号:10 (01)
被引量:4
标识
DOI:10.1117/1.jmi.10.1.014001
摘要
PurposeThe 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.ApproachCTP 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).ResultsThe 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.ConclusionsUse 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.
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