Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images_Journal of Biomedical Engineering

Authors：

LIU Naijia ¹ , HU Ying ¹ , YANG Yifeng ¹ , LI Yuehua ² ,  NIE Shengdong ¹

1. Institute of Medical Imaging Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P.R.China;
2. Department of Radiology Intervention, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, P.R. China;

Corresponding author：

Keywords：

acute ischemic stroke; computer-aided diagnosis; Alberta stroke program early computer tomography score; deep learning

DOI：

10.7507/1001-5515.202012037

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.

Citation： LIU Naijia, HU Ying, YANG Yifeng, LI Yuehua, NIE Shengdong. Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images. Journal of Biomedical Engineering, 2021, 38(4): 790-796, 804. doi: 10.7507/1001-5515.202012037 Copy

1.	Prakkamakul S, Yoo A J. ASPECTS CT in acute ischemia: review of current data. Top Magn Reson Imaging, 2017, 26(3): 103-112.
2.	Runde D. Calculated decisions: Alberta stroke program early CT score (ASPECTS). Emerg Med Pract, 2019, 21(6): CD6-CD7.
3.	Raza S A, Barreira C M, Rodrigues G M, et al. Prognostic importance of CT ASPECTS and CT perfusion measures of infarction in anterior emergent large vessel occlusions. J Neurointerv Surg, 2019, 11(7): 670-674.
4.	Powers W J, Rabinstein A A, Ackerson T, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American heart association/American stroke association. Stroke, 2019, 50(12): e344-e418.
5.	杨安权. CT技术在评估急性缺血性脑卒中患者侧支循环中的应用效果观察. 影像研究与医学应用, 2020, 4(13): 102-103.
6.	肖明霞. 磁共振成像技术在急性缺血性脑卒中患者中的应用效果分析. 中国医药指南, 2020, 18(18): 100-101.
7.	Phan K, Saleh S, Dmytriw A A, et al. Influence of ASPECTS and endovascular thrombectomy in acute ischemic stroke: a meta-analysis. J Neurointerv Surg, 2019, 11(7): 664-669.
8.	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
9.	俞益洲, 石德君, 马杰超, 等. 人工智能在医学影像分析中的应用进展. 中国医学影像技术, 2019, 35(12): 1808-1812.
10.	Jadhav AP, Wechsler LR. Patient selection for stroke thrombectomy: is CT head good enough?. Neurology, 2016, 87(3): 242-243.
11.	范茜君, 杨素君. CT灌注与CT造影对缺血性脑卒中的诊断价值. 医疗装备, 2020, 33(12): 5-6.
12.	宋磊, 高波, 沈桂权, 等. FLAIR血管高信号和DWI在缺血性脑卒中的临床价值. 临床放射学杂志, 2019, 38(9): 1613-1618.
13.	嵇碧莹, 徐运. 多模式CT对不同梗死部位的急性缺血性脑卒中的临床应用研究. 影像研究与医学应用, 2020, 4(13): 80-81.
14.	Farzin B, Fahed R, Guilbert F, et al. Early CT changes in patients admitted for thrombectomy: intrarater and interrater agreement. Neurology, 2016, 87(3): 249-256.
15.	Copen W A, Yoo A J, Rost N S, et al. In patients with suspected acute stroke, CT perfusion-based cerebral blood flow maps cannot substitute for DWI in measuring the ischemic core. PLoS One, 2017, 12(11): e0188891.
16.	Asadi H, Dowling R, Yan B, et al. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One, 2014, 9(2): e88225.
17.	Anbumozhi S. Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier. Int J Imaging Syst Technol, 2020, 30(1): 196-202.
18.	Mokin M, Primiani C T, Siddiqui A H, et al. ASPECTS (Alberta stroke program early CT score) measurement using Hounsfield unit values when selecting patients for stroke thrombectomy. Stroke, 2017, 48(6): 1574-1579.
19.	Scheldeman L, Wouters A, Boutitie F, et al. Different mismatch concepts for magnetic resonance imaging-guided thrombolysis in unknown onset stroke. Ann Neurol, 2020, 87(6): 931-938.
20.	Simonsen C Z, Madsen M H, Schmitz M L, et al. Sensitivity of diffusion- and perfusion-weighted imaging for diagnosing acute ischemic stroke is 97.5%. Stroke, 2015, 46(1): 98-101.
21.	Brugnara G, Neuberger U, Mahmutoglu M, et al. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke, 2020, 51(12): 3541-3551.
22.	Thomalla G, Simonsen C Z, Boutitie F, et al. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med, 2018, 379(7): 611-622.
23.	Ho K C, Speier W, El-Saden S, et al. Classifying acute ischemic stroke onset time using deep imaging features. AMIA Annu Symp Proc, 2018, 2017: 892-901.
24.	Chen L, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage Clin, 2017, 15: 633-643.
25.	Nielsen A, Hansen M B, Tietze A, et al. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke, 2018, 49(6): 1394-1401.
26.	Rondina J M, Filippone M, Girolami M, et al. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin, 2016, 12: 372-380.
27.	Sung S M, Kang Y J, Cho H J, et al. Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms. Clin Neurol Neurosurg, 2020, 195: 105892.
28.	Barman A, Inam M E, Lee S, et al. Determining ischemic stroke from CT-angiography imaging using symmetry-sensitive convolutional networks//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019: 1873-1877.
29.	Shaham U, R L R. Learning by coincidence: siamese networks and common variable learning. Pattern Recogn, 2018: 74.
30.	Sheth S, Lopez-Rivera V, Barman A, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography. Stroke, 2019, 50(11): 3093-3100.
31.	Tang Y. Deep learning using linear support vector machines. arXiv: 1306.0239, 2015: 0239.
32.	Nagel S, Wang X, Carcel C, et al. Clinical utility of electronic Alberta stroke program early computed tomography score software in the enchanted trial database. Stroke, 2018, 49(6): 1407-1411.
33.	Herweh C, Ringleb P A, Rauch G, et al. Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing ct scans of acute ischemic stroke patients. Int J Stroke, 2016, 11(4): 438-445.
34.	Albers G W, Wald M J, Mlynash M, et al. Automated calculation of Alberta stroke program early CT score: validation in patients with large hemispheric infarct. Stroke, 2019, 50(11): 3277-3279.
35.	Maegerlein C, Fischer J, Mönch S, et al. Automated calculation of the Alberta stroke program early CT score: feasibility and reliability. Radiology, 2019, 291(1): 141-148.
36.	Feng R, Badgeley M, Mocco J, et al. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg, 2018, 10(4): 358-362.
37.	Takahashi N, Lee Y, Tsai D Y, et al. An automated detection method for the MCA dot sign of acute stroke in unenhanced CT. Radiol Phys Technol, 2014, 7(1): 79-88.
38.	Kamal H, Lopez V, Sheth S. Machine learning in acute ischemic stroke neuroimaging. Front Neurol, 2018, 9: 945.
39.	Ferreti L A, Leitao C A, Teixeira B, et al. The use of e-ASPECTS in acute stroke care: validation of method performance compared to the performance of specialists. Arq Neuropsiquiatr, 2020, 78(12): 757-761.
40.	Philpotts L E. Can computer-aided detection be detrimental to mammographic interpretation?. Radiology, 2009, 253(1): 17-22.
41.	王坤峰, 苟超, 段艳杰, 等. 生成式对抗网络GAN的研究进展与展望. 自动化学报, 2017, 43(3): 321-332.
42.	Jin C B, Kim H, Liu M, et al. Deep CT to MR synthesis using paired and unpaired data. Sensors (Basel), 2019, 19(10): 2361.

1. Prakkamakul S, Yoo A J. ASPECTS CT in acute ischemia: review of current data. Top Magn Reson Imaging, 2017, 26(3): 103-112.
2. Runde D. Calculated decisions: Alberta stroke program early CT score (ASPECTS). Emerg Med Pract, 2019, 21(6): CD6-CD7.
3. Raza S A, Barreira C M, Rodrigues G M, et al. Prognostic importance of CT ASPECTS and CT perfusion measures of infarction in anterior emergent large vessel occlusions. J Neurointerv Surg, 2019, 11(7): 670-674.
4. Powers W J, Rabinstein A A, Ackerson T, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American heart association/American stroke association. Stroke, 2019, 50(12): e344-e418.
5. 杨安权. CT技术在评估急性缺血性脑卒中患者侧支循环中的应用效果观察. 影像研究与医学应用, 2020, 4(13): 102-103.
6. 肖明霞. 磁共振成像技术在急性缺血性脑卒中患者中的应用效果分析. 中国医药指南, 2020, 18(18): 100-101.
7. Phan K, Saleh S, Dmytriw A A, et al. Influence of ASPECTS and endovascular thrombectomy in acute ischemic stroke: a meta-analysis. J Neurointerv Surg, 2019, 11(7): 664-669.
8. 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
9. 俞益洲, 石德君, 马杰超, 等. 人工智能在医学影像分析中的应用进展. 中国医学影像技术, 2019, 35(12): 1808-1812.
10. Jadhav AP, Wechsler LR. Patient selection for stroke thrombectomy: is CT head good enough?. Neurology, 2016, 87(3): 242-243.
11. 范茜君, 杨素君. CT灌注与CT造影对缺血性脑卒中的诊断价值. 医疗装备, 2020, 33(12): 5-6.
12. 宋磊, 高波, 沈桂权, 等. FLAIR血管高信号和DWI在缺血性脑卒中的临床价值. 临床放射学杂志, 2019, 38(9): 1613-1618.
13. 嵇碧莹, 徐运. 多模式CT对不同梗死部位的急性缺血性脑卒中的临床应用研究. 影像研究与医学应用, 2020, 4(13): 80-81.
14. Farzin B, Fahed R, Guilbert F, et al. Early CT changes in patients admitted for thrombectomy: intrarater and interrater agreement. Neurology, 2016, 87(3): 249-256.
15. Copen W A, Yoo A J, Rost N S, et al. In patients with suspected acute stroke, CT perfusion-based cerebral blood flow maps cannot substitute for DWI in measuring the ischemic core. PLoS One, 2017, 12(11): e0188891.
16. Asadi H, Dowling R, Yan B, et al. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One, 2014, 9(2): e88225.
17. Anbumozhi S. Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier. Int J Imaging Syst Technol, 2020, 30(1): 196-202.
18. Mokin M, Primiani C T, Siddiqui A H, et al. ASPECTS (Alberta stroke program early CT score) measurement using Hounsfield unit values when selecting patients for stroke thrombectomy. Stroke, 2017, 48(6): 1574-1579.
19. Scheldeman L, Wouters A, Boutitie F, et al. Different mismatch concepts for magnetic resonance imaging-guided thrombolysis in unknown onset stroke. Ann Neurol, 2020, 87(6): 931-938.
20. Simonsen C Z, Madsen M H, Schmitz M L, et al. Sensitivity of diffusion- and perfusion-weighted imaging for diagnosing acute ischemic stroke is 97.5%. Stroke, 2015, 46(1): 98-101.
21. Brugnara G, Neuberger U, Mahmutoglu M, et al. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke, 2020, 51(12): 3541-3551.
22. Thomalla G, Simonsen C Z, Boutitie F, et al. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med, 2018, 379(7): 611-622.
23. Ho K C, Speier W, El-Saden S, et al. Classifying acute ischemic stroke onset time using deep imaging features. AMIA Annu Symp Proc, 2018, 2017: 892-901.
24. Chen L, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage Clin, 2017, 15: 633-643.
25. Nielsen A, Hansen M B, Tietze A, et al. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke, 2018, 49(6): 1394-1401.
26. Rondina J M, Filippone M, Girolami M, et al. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin, 2016, 12: 372-380.
27. Sung S M, Kang Y J, Cho H J, et al. Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms. Clin Neurol Neurosurg, 2020, 195: 105892.
28. Barman A, Inam M E, Lee S, et al. Determining ischemic stroke from CT-angiography imaging using symmetry-sensitive convolutional networks//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019: 1873-1877.
29. Shaham U, R L R. Learning by coincidence: siamese networks and common variable learning. Pattern Recogn, 2018: 74.
30. Sheth S, Lopez-Rivera V, Barman A, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography. Stroke, 2019, 50(11): 3093-3100.
31. Tang Y. Deep learning using linear support vector machines. arXiv: 1306.0239, 2015: 0239.
32. Nagel S, Wang X, Carcel C, et al. Clinical utility of electronic Alberta stroke program early computed tomography score software in the enchanted trial database. Stroke, 2018, 49(6): 1407-1411.
33. Herweh C, Ringleb P A, Rauch G, et al. Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing ct scans of acute ischemic stroke patients. Int J Stroke, 2016, 11(4): 438-445.
34. Albers G W, Wald M J, Mlynash M, et al. Automated calculation of Alberta stroke program early CT score: validation in patients with large hemispheric infarct. Stroke, 2019, 50(11): 3277-3279.
35. Maegerlein C, Fischer J, Mönch S, et al. Automated calculation of the Alberta stroke program early CT score: feasibility and reliability. Radiology, 2019, 291(1): 141-148.
36. Feng R, Badgeley M, Mocco J, et al. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg, 2018, 10(4): 358-362.
37. Takahashi N, Lee Y, Tsai D Y, et al. An automated detection method for the MCA dot sign of acute stroke in unenhanced CT. Radiol Phys Technol, 2014, 7(1): 79-88.
38. Kamal H, Lopez V, Sheth S. Machine learning in acute ischemic stroke neuroimaging. Front Neurol, 2018, 9: 945.
39. Ferreti L A, Leitao C A, Teixeira B, et al. The use of e-ASPECTS in acute stroke care: validation of method performance compared to the performance of specialists. Arq Neuropsiquiatr, 2020, 78(12): 757-761.
40. Philpotts L E. Can computer-aided detection be detrimental to mammographic interpretation?. Radiology, 2009, 253(1): 17-22.
41. 王坤峰, 苟超, 段艳杰, 等. 生成式对抗网络GAN的研究进展与展望. 自动化学报, 2017, 43(3): 321-332.
42. Jin C B, Kim H, Liu M, et al. Deep CT to MR synthesis using paired and unpaired data. Sensors (Basel), 2019, 19(10): 2361.

Journal of Biomedical Engineering

Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content