基於深度學習之冠狀動脈分割及其應用

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游子謙(Tzu-Chien Yu) 查詢紙本館藏

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資訊工程學系

論文名稱

基於深度學習之冠狀動脈分割及其應用
(The Development of Deep Learning-based Coronary Artery Segmentation Methods and its Applications)

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摘要(中)

根據台灣衛生福利部統計處統計，心臟疾病長年位居台灣前三大死因，其中冠狀動脈心臟病又是十分常見的心臟疾病之一。目前在冠狀動脈檢查時，經常使用非侵入性的電腦斷層掃描，透過於電腦斷層影像中標註冠狀動脈位置，可以進行後續的血管狹窄度、鈣化位置的分析，然而以人工方式進行冠狀動脈血管標註非常花費時間，因此如何自動化流程，以減少過程中所耗費的人力資源，便顯得十分重要。

除此之外，電腦斷層掃描中使用的顯影劑通常含有碘的成分，對於部分受檢者可能產生過敏或對其腎臟造成傷害，因此較不適合使用顯影劑，然而無顯影劑增強之電腦斷層掃描影像對於血管的對比度較低，顯著地增加了冠狀動脈血管的標註難度，因此如何運用無顯影劑增強之電腦斷層影像進行冠狀動脈分割，也是具有挑戰性的問題。

本研究利用深度學習技術，使用有顯影劑增強之電腦斷層影像，訓練能夠自動地從電腦斷層影像中分割冠狀動脈的模型，並且利用分割之結果開發應用，
進行鈣化位置以及血管狹窄度的分析。此外本研究也使用無顯影劑增強之電腦斷層影像進行實驗，使用深度學習技術實現一個資料擴增模型，將有顯影劑增強之電腦斷層掃描影像轉換為虛擬的無顯影劑增強之電腦斷層影像，並將產生的影像利用於輔助訓練以無顯影劑增強之電腦斷層影像進行冠狀動脈分割的模型。

實驗結果顯示，本研究能夠利用有顯影劑增強之電腦斷層影像有效的分割冠狀動脈，對於測試資料以7-fold交叉驗證能夠達到Dice Coefficient 0.7868的結果，並能夠以此分割結果進行鈣化位置以及血管狹窄度分析，給予醫師在診斷上的輔助。而在無顯影劑增強之電腦斷層影像實驗中，透過利用本研究的資料擴增方式產生的訓練資料輔助模型訓練，對於測試資料以5-fold交叉驗證能夠從Dice Coefficient 0.5158增加到0.5674，顯示了該資料擴增方法的有效性。

摘要(英)

According to the report from the Statistics Department of Taiwan’s Ministry of Health and Welfare, heart disease has been among the top three causes of death in Taiwan for many years. Among them, coronary artery disease is one of the most common heart diseases. Currently, computed tomography (CT) is often used in coronary artery examinations. By extracting the coronary arteries in the CT image,
the subsequent analysis of vascular stenosis and calcification can be carried out. However, manual labeling of coronary arteries is a time-consuming task. Therefore, how to automate the process to reduce the human resources consumed in the process is very important.

In addition, the contrast agent used in computed tomography usually contains iodine. For some subjects, the contrast agent may cause allergies or damage their kidney function, so those subjects are unsuitable for the contrast agent. However, the contrast of CT images without contrast enhancement to vessels is very low, and it significantly increases the difficulty of labeling coronary arteries. Therefore, how to use CT images without contrast enhancement for coronary artery segmentation is also a challenging problem.

This research uses CT images with contrast enhancement to train a model that can automatically segment coronary arteries from CT images and use the results of segmentation to develop applications that perform analysis of calcification location and vascular stenosis. In addition, this research also used CT images without contrast enhancement for experiments. This research uses deep learning techniques to implement a data augmentation model to convert the CT image with contrast enhancement into a virtual CT image without contrast enhancement. The generated virtual CT images are then used to assist in training a model of coronary artery segmentation with CT images without contrast enhancement.

Experimental results show that this research′s deep learning model can effectively extract coronary arteries from CT images with contrast enhancement. For the test CT images with contrast enhancement, the result of Dice Coefficient 0.7868 can be achieved. The segmentation results can be used to analyze calcification and vascular stenosis, and assist physicians in diagnosis. For the CT images without contrast enhancement experiment, the test result can be increased from Dice Coefficient 0.5158 to 0.5674 by using the training data generated by the data augmentation model of this study. The results show the effectiveness of the data augmentation model.

關鍵字(中)

★ 冠狀動脈
★ 深度學習
★ 醫學影像
★ 影像處理
★ 電腦視覺

關鍵字(英)

★ Coronary Artery
★ Deep Learning
★ Medical Image Processing
★ Image Processing
★ Computer Vision

論文目次

摘要 iv
Abstract vi
誌謝 viii
目錄 ix
一、緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 3
二、背景知識以及文獻回顧4
2.1 背景知識 4
2.1.1 電腦斷層掃描 4
2.1.2 Hounsfield Units 4
2.1.3 心臟冠狀動脈結構 6
2.1.4 3D UNet 7
2.1.5 CycleGAN 7
2.2 文獻回顧 8
2.2.1 對於有顯影劑增強之電腦斷層影像進行冠狀動脈分割之研究 8
2.2.2 對於無顯影劑增強之電腦斷層影像進行分割之研究 9
2.2.3 將GAN 應用於醫學影像之研究 9
三、研究方法 11
3.1 資料前處理 11
3.1.1 擷取HU 值範圍 11
3.1.2 影像壓縮 13
3.2 無顯影劑影像資料擴增模型 13
3.3 冠狀動脈分割 14
3.4 有顯影劑冠狀動脈分割之相關應用 15
3.4.1 鈣化位置偵測 15
3.4.2 狹窄度分析 17
3.4.3 資料視覺化 19
四、實驗設計與結果 21
4.1 資料集 21
4.2 有顯影劑增強影像之冠狀動脈分割 22
4.3 電腦斷層掃描影像風格轉換 26
4.4 無顯影劑增強影像之冠狀動脈分割 27
4.5 相關應用及視覺化 31
4.5.1 取得血管分析結果 31
4.5.2 鈣化位置分析 32
4.5.3 狹窄度分析 33
五、總結 36
5.1 結論 36
5.2 未來展望 37
參考文獻 38

參考文獻

[1] 衛生福利部統計處. (Jan. 24, 2017). “死因統計,” 衛生福利部統計處, [Online]. Available: https://dep.mohw.gov.tw/DOS/np1776113. html (visited on 06/02/2021).
[2] 衛生福利部國民健康署. (Dec. 31, 2016). “認識冠心病,” 衛生福利部國民健康署, [Online]. Available: https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=632&pid=1188 (visited on 06/02/2021).
[3] M. M. Nadrljanski. “Computed tomography | radiology reference article | radiopaedia.org,” Radiopaedia, [Online]. Available: https://radiopaedia.org/articles/computedtomography (visited on 07/05/2021).
[4] P. McKavanagh, G. Walls, C. McCune, J. Malloy, M. T. Harbinson, P. A. Ball, and P. M. Donnelly, “The essentials of cardiac computerized tomography,” Cardiology and Therapy, vol. 4, no. 2, pp. 117–129, Dec. 2015.
[5] P. Moeskops, J. M. Wolterink, B. H. M. van der Velden, K. G. A. Gilhuijs, T. Leiner, M. A. Viergever, and I. Išgum, “Deep learning for multitask medical image segmentation in multiple modalities,” in Medical Image Computing and ComputerAssisted Intervention –MICCAI 2016, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, Eds., ser. Lecture Notes in Computer Science, Cham: Springer International Publishing, 2016, pp. 478–486.
[6] W. Huang, L. Huang, Z. Lin, S. Huang, Y. Chi, J. Zhou, J. Zhang, R.S. Tan, and L. Zhong, “Coronary artery segmentation by deep learning neural networks on computed tomographic coronary angiographic images,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2018, pp. 608–611.
[7] Y.C. Chen, Y.C. Lin, C.P. Wang, C.Y. Lee, T.D. Wang, W.J. Lee, and C.M. Chen, “Coronary artery segmentation in cardiac CT angiography using 3d multichannel unet,” presented at the International Conference on Medical Imaging with Deep Learning – Extended Abstract Track, Apr. 17, 2019.
[8] T. Lei, R. Wang, Y. Wan, B. Zhang, H. Meng, and A. K. Nandi, “Medical image segmentation using deep learning: A survey,” arXiv:2009.13120 [cs, eess], Dec. 16, 2020. 38
[9] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 1, 2017.
[10] S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021.
[11] M. Andreucci, T. Faga, R. Serra, G. De Sarro, and A. Michael, “Update on the renal toxicity of iodinated contrast drugs used in clinical medicine,” Drug, Healthcare and Patient Safety, vol. 9, pp. 25–37, 2017.
[12] P. Rasuli and D. I. Hammond, “Metformin and contrast media: Where is the conflict?” Canadian Association of Radiologists Journal = Journal l’Association Canadienne Des Radiologistes, vol. 49, no. 3, pp. 161–166, Jun. 1998.
[13] M. Saljoughian. (May 22, 2012). “Intravenous radiocontrast media: A review of allergic reactions,” [Online]. Available: https : / / www . uspharmacist . com / article / intravenous radiocontrastmediaareviewofallergicreactions (visited on 07/06/2021).
[14] K. Greenway. “Hounsfield unit | radiology reference article | radiopaedia.org,” Radiopaedia, [Online]. Available: https : / / radiopaedia . org / articles / hounsfield unit (visited on 07/04/2021).
[15] M. H. Lev and R. G. Gonzalez, “CT angiography and CT perfusion imaging,” in Brain Mapping: The Methods (Second Edition), A. W. Toga and J. C. Mazziotta, Eds., San Diego: Academic Press, Jan. 1, 2002, pp. 427–484.
[16] A. Murphy. “Windowing (CT) | radiology reference article | radiopaedia.org,” Radiopaedia, [Online]. Available: https://radiopaedia.org/articles/windowingct? lang=us (visited on 07/04/2021).
[17] I. Ogobuiro, C. J. Wehrle, and F. Tuma, “Anatomy, thorax, heart coronary arteries,” in StatPearls, Treasure Island (FL): StatPearls Publishing, 2021.
[18] S. Rehman, A. Khan, and A. Rehman, Physiology, Coronary Circulation. StatPearls Publishing, May 9, 2021.
[19] “Atherosclerosis | american heart association,” [Online]. Available: https://www.heart. org/en/healthtopics/ cholesterol/aboutcholesterol/ atherosclerosis (visited on 08/22/2021).
[20] “Atherosclerosis | NHLBI, NIH,” [Online]. Available: https://www.nhlbi.nih.gov/healthtopics/ atherosclerosis (visited on 08/22/2021).
[21] N. Ojha and A. S. Dhamoon, Myocardial Infarction. StatPearls Publishing, Aug. 11, 2021. 39
[22] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d unet: Learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and ComputerAssisted Intervention–MICCAI 2016, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, Eds., ser. Lecture Notes in Computer Science, Cham: Springer International Publishing, 2016, pp. 424–432.
[23] J.Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired imagetoimage translation using cycleconsistent adversarial networks,” in 2017 IEEE International Conference on Computer Vision (ICCV), Oct. 2017, pp. 2242–2251.
[24] R. Shahzad, D. Bos, R. P. J. Budde, K. Pellikaan, W. J. Niessen, A. van der Lugt, and T. van Walsum, “Automatic segmentation and quantification of the cardiac structures from noncontrastenhanced cardiac CT scans,” Physics in Medicine and Biology, vol. 62, no. 9, pp. 3798–3813, May 7, 2017.
[25] A. Patel, F. H. B. M. Schreuder, C. J. M. Klijn, M. Prokop, B. v. Ginneken, H. A. Marquering, Y. B. W. E. M. Roos, M. I. Baharoglu, F. J. A. Meijer, and R. Manniesing, “Intracerebral haemorrhage segmentation in noncontrast CT,” Scientific Reports, vol. 9, no. 1, p. 17 858, Nov. 28, 2019.
[26] A. Tuladhar, S. Schimert, D. Rajashekar, H. C. Kniep, J. Fiehler, and N. D. Forkert, “Automatic segmentation of stroke lesions in noncontrast computed tomography datasets with convolutional neural networks,” IEEE Access, vol. 8, pp. 94 871–94 879, 2020.
[27] J. Jiang, Y.C. Hu, N. Tyagi, P. Zhang, A. Rimner, G. S. Mageras, J. O. Deasy, and H. Veeraraghavan, “Tumoraware, adversarial domain adaptation from CT to MRI for lung cancer segmentation,” in Medical Image Computing and Computer Assisted Intervention –MICCAI 2018, A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. AlberolaLópez, and G. Fichtinger, Eds., ser. Lecture Notes in Computer Science, Cham: Springer International Publishing, 2018, pp. 777–785.
[28] P. Welander, S. Karlsson, and A. Eklund, “Generative adversarial networks for imagetoimage translation on multicontrast MR images a comparison of CycleGAN and UNIT,” arXiv:1806.07777 [cs], Jun. 20, 2018.
[29] C. Song, B. He, H. Chen, S. Jia, X. Chen, and F. Jia, “Noncontrast CT liver segmentation using CycleGAN data augmentation from contrast enhanced CT,” in Interpretable and AnnotationEfficient Learning for Medical Image Computing, J. Cardoso, H. Van Nguyen, N. Heller, P. Henriques Abreu, I. Isgum, W. Silva, R. Cruz, J. Pereira Amorim, V. Patel, B. Roysam, K. Zhou, S. Jiang, N. Le, K. Luu, R. Sznitman, V. Cheplygina, D. Mateus, E. Trucco, and S. Abbasi, Eds., ser. Lecture Notes in Computer Science, Cham: Springer International Publishing, 2020, pp. 122–129.
[30] A. Kanitsar, D. Fleischmann, R. Wegenkittl, P. Felkel, and E. Groller, “CPR curved planar reformation,” in IEEE Visualization, 2002. VIS 2002., Oct. 2002, pp. 37–44.
[31] The Slicer Community. “3d slicer image computing platform,” 3D Slicer, [Online]. Available: https://slicer.org/ (visited on 07/02/2021). 40
[32] A. Fedorov, R. Beichel, J. KalpathyCramer, J. Finet, J.C. FillionRobin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V. Miller, S. Pieper, and R. Kikinis, “3d slicer as an image computing platform for the quantitative imaging network,” Magnetic resonance imaging, vol. 30, no. 9, pp. 1323–1341, Nov. 2012.
[33] D. T. Gering, A. Nabavi, R. Kikinis, W. E. L. Grimson, N. Hata, P. Everett, F. Jolesz, and W. M. Wells, “An integrated visualization system for surgical planning and guidance using image fusion and interventional imaging,” in Medical Image Computing and ComputerAssisted Intervention–MICCAI＇99, C. Taylor and A. Colchester, Eds., ser. Lecture Notes in Computer Science, Berlin, Heidelberg: Springer, 1999, pp. 809–819.
[34] D. T. Gering, A. Nabavi, R. Kikinis, N. Hata, L. J. O’Donnell, W. E. Grimson, F. A. Jolesz, P. M. Black, and W. M. Wells, “An integrated visualization system for surgical planning and guidance using image fusion and an open MR,” Journal of magnetic resonance imaging: JMRI, vol. 13, no. 6, pp. 967–975, Jun. 2001.
[35] T. Kapur, S. Pieper, A. Fedorov, J.C. FillionRobin, M. Halle, L. O’Donnell, A. Lasso, T. Ungi, C. Pinter, J. Finet, S. Pujol, J. Jagadeesan, J. Tokuda, I. Norton, R. S. J. Estepar, D. Gering, H. J. W. L. Aerts, M. Jakab, N. Hata, L. Ibanez, D. Blezek, J. Miller, S. Aylward, W. E. L. Grimson, G. Fichtinger, W. M. Wells, W. E. Lorensen, W. Schroeder, and R. Kikinis, “Increasing the impact of medical image computing using communitybased openaccess hackathons: The NAMIC and 3d slicer experience,” Medical Image Analysis, 20th anniversary of the Medical Image Analysis journal (MedIA), vol. 33, pp. 176– 180, Oct. 1, 2016.
[36] R. Kikinis, S. D. Pieper, and K. G. Vosburgh, “3d slicer: A platform for subjectspecific image analysis, visualization, and clinical support,” in Intraoperative Imaging and ImageGuided Therapy, F. A. Jolesz, Ed., New York, NY: Springer, 2014, pp. 277–289.
[37] S. Pieper, B. Lorensen, W. Schroeder, and R. Kikinis, “The NAMIC kit: ITK, VTK, pipelines, grids and 3d slicer as an open platform for the medical image computing community,” in 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., Apr. 2006, pp. 698–701.

指導教授

蘇木春(Mu-Chun Su)

審核日期

2021-8-25

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