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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳中明 | zh_TW |
dc.contributor.advisor | Chung-Ming Chen | en |
dc.contributor.author | 林彥廷 | zh_TW |
dc.contributor.author | Yan-Ting Lin | en |
dc.date.accessioned | 2023-03-19T21:15:31Z | - |
dc.date.available | 2023-12-26 | - |
dc.date.copyright | 2022-08-22 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | S. Z. Goldhaber and H. Bounameaux, "Pulmonary embolism and deep vein thrombosis," The Lancet, vol. 379, no. 9828, pp. 1835-1846, 2012. N. Kucher, "Deep-vein thrombosis of the upper extremities," New England Journal of Medicine, vol. 364, no. 9, pp. 861-869, 2011. M. D. Silverstein, J. A. Heit, D. N. Mohr, T. M. Petterson, W. M. O'Fallon, and L. J. Melton, "Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study," Archives of internal medicine, vol. 158, no. 6, pp. 585-593, 1998. J. Bĕlohlávek, V. Dytrych, and A. Linhart, "Pulmonary embolism, part I: Epidemiology, risk factors and risk stratification, pathophysiology, clinical presentation, diagnosis and nonthrombotic pulmonary embolism," Experimental & Clinical Cardiology, vol. 18, no. 2, p. 129, 2013. M. Turetz, A. T. Sideris, O. A. Friedman, N. Triphathi, and J. M. Horowitz, "Epidemiology, pathophysiology, and natural history of pulmonary embolism," in Seminars in interventional radiology, 2018, vol. 35, no. 02: Thieme Medical Publishers, pp. 92-98. G. Sadigh, A. M. Kelly, and P. Cronin, "Challenges, controversies, and hot topics in pulmonary embolism imaging," American Journal of Roentgenology, vol. 196, no. 3, pp. 497-515, 2011. Tintinalli et al., Tintinalli's emergency medicine : a comprehensive study guide, 8th edition ed. (McGraw-Hill's AccessMedicine). New York, N.Y: McGraw-Hill Education LLC (in English), 2016. A. Ahmad, E. Subkovas, and J. Ryan, "Pulmonary embolism presenting as non-ST elevation myocardial infarction: a case report," Cases journal, vol. 2, no. 1, pp. 1-4, 2009. E. Bruinstroop, M. Van de Ree, and M. Huisman, "The use of D-dimer in specific clinical conditions: a narrative review," European journal of internal medicine, vol. 20, no. 5, pp. 441-446, 2009. G. Agnelli and C. Becattini, "Acute pulmonary embolism," New England Journal of Medicine, vol. 363, no. 3, pp. 266-274, 2010. A. T. F. Members et al., "Guidelines on the diagnosis and management of acute pulmonary embolism: the Task Force for the Diagnosis and Management of Acute Pulmonary Embolism of the European Society of Cardiology (ESC)," European heart journal, vol. 29, no. 18, pp. 2276-2315, 2008. S. D. Qanadli et al., "Pulmonary embolism detection: prospective evaluation of dual-section helical CT versus selective pulmonary arteriography in 157 patients," Radiology, vol. 217, no. 2, pp. 447-455, 2000. J. Michiels, H. Hoogsteden, and P. Pattynama, "Non-invasive diagnosis of pulmonary embolism, anno 2005," Acta Chirurgica Belgica, vol. 105, no. 1, pp. 26-34, 2005. N. J. Screaton et al., "Detection of lung perfusion abnormalities using computed tomography in a porcine model of pulmonary embolism," Journal of thoracic imaging, vol. 18, no. 1, pp. 14-20, 2003. U. S. Yavas, C. Calisir, and I. R. Ozkan, "The interobserver agreement between residents and experienced radiologists for detecting pulmonary embolism and DVT with using CT pulmonary angiography and indirect CT venography," Korean journal of radiology, vol. 9, no. 6, pp. 498-502, 2008. H. Bouma, J. J. Sonnemans, A. Vilanova, and F. A. Gerritsen, "Automatic detection of pulmonary embolism in CTA images," IEEE transactions on medical imaging, vol. 28, no. 8, pp. 1223-1230, 2009. X. Wang, X. Song, B. E. Chapman, and B. Zheng, "Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm," in Medical Imaging 2012: Computer-Aided Diagnosis, 2012, vol. 8315: International Society for Optics and Photonics, p. 83152U. C. Zhou et al., "Preliminary investigation of computer-aided detection of pulmonary embolism in three-dimensional computed tomography pulmonary angiography Images1," Academic radiology, vol. 12, no. 6, pp. 782-792, 2005. C. Zhou et al., "Computer‐aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets," Medical physics, vol. 36, no. 8, pp. 3385-3396, 2009. Y. Masutani, H. MacMahon, and K. Doi, "Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis," IEEE Transactions on Medical Imaging, vol. 21, no. 12, pp. 1517-1523, 2002. H.-H. Tsai, C.-L. Chin, and Y.-C. Cheng, "Intelligent Pulmonary Embolism Detection System," Biomedical Engineering: Applications, Basis and Communications, vol. 24, no. 06, pp. 471-483, 2012. J. Fairfield, "Toboggan contrast enhancement for contrast segmentation," in [1990] Proceedings. 10th International Conference on Pattern Recognition, 1990, vol. 1: IEEE, pp. 712-716. J. Liang and J. Bi, "Computer aided detection of pulmonary embolism with tobogganing and mutiple instance classification in CT pulmonary angiography," in Biennial International Conference on Information Processing in Medical Imaging, 2007: Springer, pp. 630-641. M. M. Dundar, G. Fung, B. Krishnapuram, and R. B. Rao, "Multiple-instance learning algorithms for computer-aided detection," IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1015-1021, 2008. S.-C. B. Lo, J.-S. Lin, M. T. Freedman, and S. K. Mun, "Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network," in Medical Imaging 1993: Image Processing, 1993, vol. 1898: International Society for Optics and Photonics, pp. 859-869. J. A. Dunnmon, D. Yi, C. P. Langlotz, C. Ré, D. L. Rubin, and M. P. Lungren, "Assessment of convolutional neural networks for automated classification of chest radiographs," Radiology, vol. 290, no. 2, pp. 537-544, 2019. D. B. Larson, M. C. Chen, M. P. Lungren, S. S. Halabi, N. V. Stence, and C. P. Langlotz, "Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs," Radiology, vol. 287, no. 1, pp. 313-322, 2018. P. Rajpurkar et al., "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLoS medicine, vol. 15, no. 11, p. e1002686, 2018. A. Park et al., "Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model," JAMA network open, vol. 2, no. 6, pp. e195600-e195600, 2019. N. Bien et al., "Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet," PLoS medicine, vol. 15, no. 11, p. e1002699, 2018. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, pp. 1097-1105, 2012. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014. C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. L. Agharezaei et al., "The prediction of the risk level of pulmonary embolism and deep vein thrombosis through artificial neural network," Acta Informatica Medica, vol. 24, no. 5, p. 354, 2016. G. Serpen, D. Tekkedil, and M. Orra, "A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis," Computers in biology and medicine, vol. 38, no. 2, pp. 204-220, 2008. N. Tajbakhsh, M. B. Gotway, and J. Liang, "Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: Springer, pp. 62-69. RSNA, "RSNA STR Pulmonary Embolism Detection," 2020. [Online]. Available: https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection/. G. Xu, "RSNA-STR-Pulmonary-Embolism-Detection." [Online]. Available: https://github.com/GuanshuoXu/RSNA-STR-Pulmonary-Embolism-Detection. J. Bi and J. Liang, "Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure," in 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007: IEEE, pp. 1-8. G. González et al., "Computer aided detection for pulmonary embolism challenge (cad-pe)," arXiv preprint arXiv:2003.13440, 2020. N. Tajbakhsh, J. Y. Shin, M. B. Gotway, and J. Liang, "Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation," Medical image analysis, vol. 58, p. 101541, 2019. T. Weikert et al., "Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm," European Radiology, vol. 30, no. 12, pp. 6545-6553, 2020. W. Liu et al., "Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning," European radiology, vol. 30, no. 6, pp. 3567-3575, 2020. S.-C. Huang et al., "PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging," Npj Digit Med, vol. 3, no. 1, pp. 1-9, 2020. E. Colak et al., "The RSNA pulmonary embolism CT dataset," Radiology: Artificial Intelligence, vol. 3, no. 2, p. e200254, 2021. M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning, 2019: PMLR, pp. 6105-6114. J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132-7141. S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, "Aggregated residual transformations for deep neural networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1492-1500. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. J. Hertz, A. Krogh, R. G. Palmer, and H. Horner, "Introduction to the theory of neural computation," Physics Today, vol. 44, no. 12, p. 70, 1991. M. Lin, Q. Chen, and S. Yan, "Network in network," arXiv preprint arXiv:1312.4400, 2013. Darragh, "RSNA Intracranial Hemorrhage Detection." [Online]. Available: https://github.com/darraghdog/rsna. S. Bai, J. Z. Kolter, and V. Koltun, "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling," arXiv preprint arXiv:1803.01271, 2018. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555, 2014. Clay, "Graphical Introduction Note About GRU." [Online]. Available: https://clay-atlas.com/us/blog/2021/07/27/gru-en-introduction-note/. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "Cbam: Convolutional block attention module," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3-19. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83722 | - |
dc.description.abstract | 肺栓塞為目前三種最常見的心血管疾病之一,其具有發病率高、臨床診斷率低與死亡率高的特性,隨著人口老化與生活水準上升等因素,肺栓塞的發病率逐年上升,且其發病的時間十分快速,需要緊急的治療,因此導致其未治療的30%高死亡率,若能及時進行診斷並加以治療,死亡率能降至2-10%,然而肺栓塞的診斷有其難度且耗時,電腦輔助診斷系統的引進可以幫助臨床醫師提供客觀的建議,以協助臨床醫師進行決策。 因此本研究開發一套基於深度學習的兩階段肺栓塞偵測分類演算法,針對肺栓塞的陽/陰性、急/慢性與出現的位置進行分類,使用RSNA STR Pulmonary Embolism Detection競賽中的CT Dataset作為樣本,演算法的第一階段為輸入CTPA二維影像以SE-ResNeXt-50進行特徵提取與初步肺栓塞陽陰性之分類,使用5-fold交叉驗證得到的平均AUC為0.962±0.003,而第二階段為輸入第一階段提取之特徵以本研究提出的PE-TCN模型針對整個案例的肺栓塞相關標籤進行分類,同時提升二維影像肺栓塞標籤分類的性能,另外本研究提出PE Weighting的方法加入到PE-TCN模型中,使用5-fold交叉驗證得到的Negative Exam for PE標籤平均AUC為0.924±0.007,Left-sided PE標籤平均AUC為0.908±0.013,Central PE標籤平均AUC為0.951±0.009,Right-sided PE標籤平均AUC為0.924±0.011,Chronic PE標籤平均AUC為0.654±0.025,Acute and Chronic PE標籤平均AUC為0.855±0.025,PE Present on Image標籤平均AUC為0.970±0.004,與常被用來處理序列問題的GRU模型進行比較,GRU模型使用5-fold交叉驗證得到的Negative Exam for PE標籤平均AUC為0.913±0.004,Left-sided PE標籤平均AUC為0.900±0.006,Central PE標籤平均AUC為0.938±0.009,Right-sided PE標籤平均AUC為0.917±0.013,Chronic PE標籤平均AUC為0.632±0.030,Acute and Chronic PE標籤平均AUC為0.843±0.028,PE Present on Image標籤平均AUC為0.925±0.039,PE-TCN模型的分類結果明顯優於GRU模型,因此,研究結果證明PE-TCN模型適用於肺栓塞相關的分類,本研究提出之基於深度學習的兩階段肺栓塞偵測分類演算法能有效協助醫師進行CTPA肺栓塞之診斷。 | zh_TW |
dc.description.abstract | Pulmonary embolism is one of the three most common cardiovascular diseases. It has high morbidity, low clinical diagnosis rate and high mortality. With factors such as population aging and living standards, the incidence of pulmonary embolism is increasing year by year. Its time of onset is very fast and it requires urgent treatment. Therefore, it has high mortality rate of 30% without treatment. If timely diagnosis and treatment can be carried out, the mortality rate can be reduced to 2-10%. However, the diagnosis of pulmonary embolism is difficult and time-consuming. The use of computer-aided diagnosis systems can help physicians provide objective recommendations to assist physicians in decision-making. Therefore, this study will develop a deep learning based two-stage pulmonary embolism detection and classification algorithm to classify the positive/negative, acute/chronic, and location of pulmonary embolism. We use the CT Dataset by the RSNA STR Pulmonary Embolism Detection competition. The first stage of the algorithm inputs 2D CTPA images to extract features and preliminarily classify pulmonary embolism positive and negative with SE-ResNeXt-50 model. The average AUC using 5-fold cross-validation is 0.962±0.003. The second stage of the algorithm inputs the features extracted in the first stage to classify the study-level pulmonary embolism related labels and improves performance of 2D images pulmonary embolism classification with PE-TCN model proposed in this study. In addition, we propose the PE Weighting method adding to the PE-TCN model. The average AUC of Negative Exam for PE label is 0.924±0.007, the average AUC of Left-sided PE label is 0.908±0.013, the average AUC of Central PE label is 0.951±0.009, the average AUC of Right-sided PE label is 0.924±0.011, the average AUC of Chronic PE label is 0.654±0.025, the average AUC of Acute and Chronic PE label is 0.855±0.025, and the average AUC of PE Present on Image label is 0.970±0.004. We compare TCN-PE with GRU which is often used to deal with sequence problems, the average AUC of the Negative Exam for PE label obtained by GRU is 0.913±0.004, the average AUC of the Left-sided PE label is 0.900±0.006, the average AUC of the Central PE label is 0.938±0.009, the average AUC of the Right-sided PE label is 0.917±0.013, the average AUC of the Chronic PE label is 0.632±0.030, the average AUC of the Acute and Chronic PE label is 0.843±0.028, and the average AUC of the PE Present on Image label is 0.925±0.039. The classification results of the TCN-PE model are significantly better than the GRU model. Therefore, the research results prove that the PE-TCN model is suitable for the classification of pulmonary embolism. The deep learning based two-stage pulmonary embolism detection and classification algorithm proposed in this study can effectively assist physicians to diagnose CTPA pulmonary embolism. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T21:15:31Z (GMT). No. of bitstreams: 1 U0001-0808202204042400.pdf: 4072682 bytes, checksum: c963576ff6d13dec7d4e9c3b26347682 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 誌謝 I 摘要 II Abstract IV 目錄 VI 圖目錄 VIII 表目錄 X 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 6 第二章 文獻回顧 7 2.1 基於傳統機器學習肺栓塞偵測分類演算法 7 2.2 基於深度學習肺栓塞偵測分類演算法 8 第三章 研究材料與方法 10 3.1 研究材料 10 3.2 研究方法 11 3.2.1 影像前處理 12 3.2.1.1 Lung localizer 12 3.2.2 Image-level feature extraction 13 3.2.2.1 SE-ResNeXt-50 14 3.2.2.1.1 Convolution block (Conv block) 15 3.2.2.1.2 Max pooling 18 3.2.2.1.3 Fully connected layer (FC) 19 3.2.2.1.4 Global average pooling (GAP) 20 3.2.2.1.5 SE-ResNeXt block 20 3.2.2.2 Binary cross-entropy 24 3.2.3 Study-level classification 24 3.2.3.1 Pre-processing 24 3.2.3.2 PE deep learning classification model 25 3.2.3.2.1 Gated Recurrent Units (GRU) 26 3.2.3.2.2 PE-TCN 28 3.2.3.3 Post-processing 34 3.3 Performance metrics 35 第四章 研究結果與討論 39 4.1 Image-level feature extraction SE-ResNeXt-50之分類結果與討論 39 4.2 Study-level classification之分類結果與討論 42 4.2.1 GRU之分類結果與討論 44 4.2.2 PE-TCN之分類結果與討論 48 4.2.3 PE-TCN with Spatial Attention Module之分類結果與討論 52 4.2.4 PE-TCN with PE Weighting之分類結果與討論 56 4.2.5 PE-TCN with Spatial Attention Module and PE Weighting之分類結果與討論 60 4.2.6 統計分析之結果與討論 64 第五章 結論與未來展望 67 5.1 結論 67 5.2 未來展望 67 參考文獻 69 | - |
dc.language.iso | zh_TW | - |
dc.title | 電腦斷層肺血管攝影之肺栓塞偵測與分類:深度學習模型之建構 | zh_TW |
dc.title | Detection and Classification of Pulmonary Embolism in CTPA Images Using Deep Learning Model | en |
dc.type | Thesis | - |
dc.date.schoolyear | 110-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 林孟暐;李佳燕;陳正剛 | zh_TW |
dc.contributor.oralexamcommittee | Mone-Wei Lin;Chia-Yen Lee;Argon Chen | en |
dc.subject.keyword | 電腦斷層肺血管攝影,肺栓塞,深度學習,時間卷積網絡, | zh_TW |
dc.subject.keyword | computed tomography pulmonary angiography,pulmonary embolism,deep learning,temporal convolutional network, | en |
dc.relation.page | 73 | - |
dc.identifier.doi | 10.6342/NTU202202129 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2022-08-11 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 醫學工程學系 | - |
顯示於系所單位: | 醫學工程學研究所 |
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