使用深度学习在CTA扫描卷下实现主动脉夹层分类和直径测量的双功能系统

Zhihui Huang; Rui Wang; Hui Yu; Yifan Xu; Cheng Cheng; Guangwei Wang; Haosen Cao; Xiang Wei; Hai-Tao Zhang

doi:10.1016/j.eng.2023.11.014

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工程（英文） ›› 2024, Vol. 34 ›› Issue (3) : 83-91. DOI: 10.1016/j.eng.2023.11.014

研究论文

Article

使用深度学习在CTA扫描卷下实现主动脉夹层分类和直径测量的双功能系统

Zhihui Huang ^a ,
Rui Wang ^b ,
Hui Yu ^c ,
Yifan Xu ^a ,
Cheng Cheng ^a ,
Guangwei Wang ^a ,
Haosen Cao ^a ,
Xiang Wei ^b^,^* ,
Hai-Tao Zhang ^a^,^*

作者信息 +

A Dual-Functional System for the Classification and Diameter Measurement of Aortic Dissections Using CTA Volumes via Deep Learning

Zhihui Huang ^a^,^# ,
Rui Wang ^b^,^# ,
Hui Yu ^c^,^# ,
Yifan Xu ^a ,
Cheng Cheng ^a ,
Guangwei Wang ^a ,
Haosen Cao ^a ,
Xiang Wei ^b^,^* ,
Hai-Tao Zhang ^a^,^*

Author information +

History +

Abstract

Acute aortic dissection is one of the most life-threatening cardiovascular diseases, with a high mortality rate. Its prevalence ranges from 0.2% to 0.8% in humans, resulting in a significant number of deaths due to being missed in manual examinations. More importantly, the aortic diameter—a critical indicator for surgical selection—significantly influences the outcomes of surgeries post-diagnosis. Therefore, it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic diameter. This paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system (DDAsys) that enables both accurate classification of aortic dissection and precise diameter measurement of the aorta. To this end, we created a dataset containing 61 190 computed tomography angiography (CTA) images from 279 patients from the Division of Cardiovascular Surgery at Tongji Hospital, Wuhan, China. The dataset provides a slice-level summary of difficult-to-identify features, which helps to improve the accuracy of both recognition and classification. Our system achieves a recognition F₁ score of 0.984, an average classification F₁ score of 0.935, and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error (RMSE). The high consistency (88.6%) between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt, precise, and consistent treatment strategy.

Keywords

Aortic dissections / Computed tomography angiography / Classification / Deep learning

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Zhihui Huang, Rui Wang, Hui Yu. . Engineering. 2024, 34(3): 83-91 https://doi.org/10.1016/j.eng.2023.11.014

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[1]	H. Kamel, M.J. Roman, A. Pitcher, R.B. Devereux. Pregnancy and the risk of aortic dissection or rupture: a cohort-crossover analysis. Circulation, 134 (7) (2016), pp. 527-533.
[2]	J. Zhao, J. Zhao, S. Pang, Q. Feng. Segmentation of the true lumen of aorta dissection via morphology-constrained stepwise deep mesh regression. IEEE Trans Med Imaging, 41 (7) (2022), pp. 1826-1836.
[3]	D. He, A. Mao, C.B. Zheng, H. Kan, K. Zhang, Z. Zhang, et al. Aortic heterogeneity across segments and under high fat/salt/glucose conditions at the single-cell level. Natl Sci Rev, 7 (5) (2020), pp. 881-896.
[4]	C.T. Nguyen, C.S. Hall, S.A. Wickline. Characterization of aortic microstructure with ultrasound: implications for mechanisms of aortic function and dissection. IEEE Trans Ultrason Ferroelectr Freq Control, 49 (11) (2002), pp. 1561-1571.
[5]	E.M. Isselbacher, O. Preventza, J.H. Black III, J.G. Augoustides, A.W. Beck, M.A. Bolen, et al. 2022 ACC/AHA guideline for the diagnosis and management of aortic disease: a report of the American heart association/American college of cardiology joint committee on clinical practice guidelines. J Am Coll Cardiol, 80 (24) (2022), pp. e223-e393.
[6]	W.K. Cheung, R. Bell, A. Nair, L.J. Menezes, R. Patel, S. Wan, et al. A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning. IEEE Access, 9 (2021), pp. 108873-108888.
[7]	J. Brunet, B. Pierrat, P. Badel. A parametric study on factors influencing the onset and propagation of aortic dissection using the extended finite element method. IEEE Trans Biomed Eng, 68 (10) (2021), pp. 2918-2929.
[8]	P. Nazerian, C. Mueller, A.M. Soeiro, B.A. Leidel, S.A.T. Salvadeo, F. Giachino, et al. Diagnostic accuracy of the aortic dissection detection risk score plus D-dimer for acute aortic syndromes: the ADvISED prospective multicenter study. Circulation, 137 (3) (2018), pp. 250-258.
[9]	G. Rong, A. Mendez, E.B. Assi, B. Zhao, M. Sawan. Artificial intelligence in healthcare: review and prediction case studies. Engineering, 6 (3) (2020), pp. 291-301.
[10]	A. Pepe, J. Li, M. Rolf-Pissarczyk, C. Gsaxner, X. Chen, G.A. Holzapfel, et al. Detection, segmentation, simulation and visualization of aortic dissections: a review. Med Image Anal, 65 (2020), 101773.
[11]	O.C. Avila-Montes, U. Kurkure, R. Nakazato, D.S. Berman, D. Dey, I.A. Kakadiaris. Segmentation of the thoracic aorta in noncontrast cardiac CT images. IEEE J Biomed Health Inform, 17 (5) (2013), pp. 936-949.
[12]	J. Zhao, Q. Feng. Automatic aortic dissection centerline extraction via morphology-guided CRN tracker. IEEE J Biomed Health Inform, 25 (9) (2021), pp. 3473-3485.
[13]	Pepe A, Egger J, Codari M, Willemink MJ, Gsaxner C, Li J, et al. Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations. 2021. arXiv:2111.11269.
[14]	L.D. Hahn, G. Mistelbauer, K. Higashigaito, M. Koci, M.J. Willemink, A.M. Sailer, et al. CT-based true- and false-lumen segmentation in type B aortic dissection using machine learning. Radiol Cardiothorac Imaging, 2 (3) (2020), e190179.
[15]	Vladimir I, Alexey S. TernausNet: U-Net with VGG11 encoder pre-trained on imageNet for image segmentation. 2018. arXiv:1801.05746.
[16]	D. Chen, X. Zhang, Y. Mei, F. Liao, H. Xu, Z. Li, et al. Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification. Med Image Anal, 69 (2021), 101931.
[17]	M. Gayhart, H. Arisawa. Automated detection of healthy and diseased aortae from images obtained by contrast-enhanced CT scan. Comput Math Methods Med, 2013 (2013), 107871.
[18]	Dehghan E, Wang H, Syeda-Mahmood T. Automatic detection of aortic dissection in contrast-enhanced CT. In: Proceedings of the 14th International Symposium on Biomedical Imaging (ISBI); 2017 Apr 18-21; Melbourne, VIC, Australia. New York City: IEEE; 557-60.
[19]	Xu X, He Z, Niu K, Zhang Y, Tang H. An automatic detection scheme of acute Stanford type A aortic dissection based on DCNNs in CTA images. In:Proceedings of the 4th International Conference on Multimedia Systems and Signal Processing (ICMSSP); 2019 May 10-12; Guangzhou, China; 2019. p. 16-20.
[20]	J. Cheng, S. Tian, L. Yu, X. Ma, Y. Xing. A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection. Biomed Signal Process Control, 62 (2020), 102145.
[21]	Yellapragada MS, Xie Y, Graf B, Richmond D, Krishnan A, Sitek A. Deep learning based detection of acute aortic syndrome in contrast CT images. In: Proceedings of the 17th International Symposium on Biomedical Imaging (ISBI); 2020 Apr 3-7; Iowa City, IA, USA. New York City: IEEE; 2020. p. 1474-7.
[22]	X. Xiong, Y. Ding, C. Sun, Z. Zhang, X. Guan, T. Zhang, et al. A cascaded Multi-task generative framework for detecting aortic dissection on 3D non-contrast-enhanced computed tomography. IEEE J Biomed Health Inform, 26 (10) (2022), pp. 5177-5188.
[23]	S. Wörz, H. von Tengg-Kobligk, V. Henninger, F. Rengier, H. Schumacher, D. Böckler, et al. 3-D quantification of the aortic arch morphology in 3-D CTA data for endovascular aortic repair. IEEE Trans Biomed Eng, 57 (10) (2010), pp. 2359-2368.
[24]	J. Wang, J. Zhao, Y. Ma, B. Huang, D. Yuan, Y. Yang. Midterm prognosis of type B aortic dissection with and without dissecting aneurysm of descending thoracic aorta after endovascular repair. Sci Rep, 9 (1) (2019), p. 8870.
[25]	A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.C. Fillion-Robin, S. Pujol, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging, 30 (9) (2012), pp. 1323-1341.
[26]	Y. Yu, S.T. Acton. Speckle reducing anisotropic diffusion. IEEE Trans Image Process, 11 (11) (2002), pp. 1260-1270.
[27]	K. Krissian, C.F. Westin, R. Kikinis, K.G. Vosburgh. Oriented speckle reducing anisotropic diffusion. IEEE Trans Image Process, 16 (5) (2007), pp. 1412-1424.
[28]	J. Li, Z.L. Yu, Z. Gu, H. Liu, Y. Li. Dilated-Inception Net: multi-scale feature aggregation for cardiac right ventricle segmentation. IEEE Trans Biomed Eng, 66 (12) (2019), pp. 3499-3508.
[29]	Vesal S, Ravikumar N, Maier A. A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT. 2018. arXiv:1905.07710.
[30]	Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2015 Oct 5-9; Munich, Germany; 2015. p. 234-41.
[31]	L.R. Dice. Measures of the amount of ecologic association between species. Ecology, 26 (3) (1945), pp. 297-302.
[32]	A.K. Golla, D.F. Bauer, R. Schmidt, T. Russ, D. Norenberg, K. Chung, et al. Convolutional neural network ensemble segmentation with ratio-based sampling for the arteries and veins in abdominal CT scans. IEEE Trans Biomed Eng, 68 (5) (2021), pp. 1518-1526.
[33]	F. Shi, Q. Yang, X. Guo, T.A. Qureshi, Z. Tian, H. Miao, et al. Intracranial vessel wall segmentation using convolutional neural networks. IEEE Trans Biomed Eng, 66 (10) (2019), pp. 2840-2847.
[34]	K. Han, L. Liu, Y. Song, Y. Liu, C. Qiu, Y. Tang, et al. An effective semi-supervised approach for liver CT image segmentation. IEEE J Biomed Health Inform, 26 (8) (2022), pp. 3999-4007.
[35]	B.D. Bodell, A.C. Taylor, P.J. Patel. Thoracic endovascular aortic repair: review of current devices and treatments options. Tech Vasc Interv Radiol, 21 (3) (2018), pp. 137-145.
[36]	C.J. Preetha, H. Meredig, G. Brugnara, M.A. Mahmutoglu, M. Foltyn, F. Isensee, et al. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health, 3 (12) (2021), pp. e784-e794.
[37]	X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, et al. A deep learning system to screen novel coronavirus disease; 2019 pneumonia. Engineering, 6 (10) (2020), pp. 1122-1129.
[38]	H. Zhu, C. Cheng, H. Yin, X. Li, P. Zuo, J. Ding, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. Lancet Digit Health, 2 (7) (2020), pp. e348-e357.
[39]	He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) 2016 Jun 26-Jul 1; Las Vegas, NV, USA. IEEE; 2016. p. 770-8.
[40]	A.S. Chin, D. Fleischmann. State-of-the-art computed tomography angiography of acute aortic syndrome. Semin Ultrasound CT MR, 33 (3) (2012), pp. 222-234.
[41]	S. Liu, Y. Wang, X. Yang, B. Lei, L. Liu, S.X. Li, et al. Deep learning in medical ultrasound analysis: a review. Engineering, 5 (2) (2019), pp. 261-275.