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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳永耀 | zh_TW |
| dc.contributor.advisor | Yung-Yaw Chen | en |
| dc.contributor.author | 林聖邦 | zh_TW |
| dc.contributor.author | Sheng-Bang Lin | en |
| dc.date.accessioned | 2023-08-15T18:02:48Z | - |
| dc.date.available | 2024-02-27 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | J. Wickham, "The new surgery," British Medical Journal (Clinical research ed.), vol. 295, no. 6613, p. 1581, 1987.
M. Morino, I. Morra, E. Rosso, C. Miglietta, and C. Garrone, "Laparoscopic vs open hepatic resection: a comparative study," Surgical Endoscopy and Other Interventional Techniques, vol. 17, no. 12, pp. 1914-1918, 2003. K. T. Nguyen, J. W. Marsh, A. Tsung, J. J. L. Steel, T. C. Gamblin, and D. A. Geller, "Comparative benefits of laparoscopic vs open hepatic resection: a critical appraisal," Archives of surgery, vol. 146, no. 3, pp. 348-356, 2011. R. Mirnezami et al., "Short-and long-term outcomes after laparoscopic and open hepatic resection: systematic review and meta-analysis," Hpb, vol. 13, no. 5, pp. 295-308, 2011. M. Feuerstein, T. Mussack, S. M. Heining, and N. Navab, "Intraoperative laparoscope augmentation for port placement and resection planning in minimally invasive liver resection," IEEE Transactions on Medical Imaging, vol. 27, no. 3, pp. 355-369, 2008. J. H. Palep, "Robotic assisted minimally invasive surgery," Journal of minimal access surgery, vol. 5, no. 1, p. 1, 2009. Y.-M. Wu, R.-H. Hu, H.-S. Lai, and P.-H. Lee, "Robotic-assisted minimally invasive liver resection," Asian journal of surgery, vol. 37, no. 2, pp. 53-57, 2014. L. Adhami and È. Coste-Manière, "Optimal planning for minimally invasive surgical robots," IEEE Transactions on Robotics and Automation, vol. 19, no. 5, pp. 854-863, 2003. Y. Hayashi, K. Misawa, and K. Mori, "Optimal port placement planning method for laparoscopic gastrectomy," International Journal of Computer Assisted Radiology and Surgery, vol. 12, pp. 1677-1684, 2017. H.-H. Lu, "Evaluation of Surgeon Comfort for Port Placement Planning in Minimally Invasive Surgery," Master, National Taiwan University, 2015. T. Kitasaka, K. Mori, Y. Hayashi, Y. Suenaga, M. Hashizume, and J.-i. Toriwaki, "Virtual pneumoperitoneum for generating virtual laparoscopic views based on volumetric deformation," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2004: Springer, pp. 559-567. J. Bano et al., "Simulation of pneumoperitoneum for laparoscopic surgery planning," in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part I 15, 2012: Springer, pp. 91-98. Y. Nimura et al., "Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning," Journal of Medical Imaging, vol. 2, no. 4, p. 044004, 2015. O. Oktay et al., "Biomechanically driven registration of pre-to intra-operative 3D images for laparoscopic surgery," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013: Springer, pp. 1-9. S. Dawda, M. Camara, P. Pratt, J. Vale, A. Darzi, and E. Mayer, "Patient-specific simulation of pneumoperitoneum for laparoscopic surgical planning," Journal of medical systems, vol. 43, no. 10, pp. 1-9, 2019. D. E. Ott, "Abdominal compliance and laparoscopy: a review," JSLS: Journal of the Society of Laparoendoscopic Surgeons, vol. 23, no. 1, 2019. A. R. Blaser, M. Björck, B. De Keulenaer, and A. Regli, "Abdominal compliance: a bench-to-bedside review," Journal of Trauma and Acute Care Surgery, vol. 78, no. 5, pp. 1044-1053, 2015. M. L. Malbrain, Y. Peeters, and R. Wise, "The neglected role of abdominal compliance in organ-organ interactions," Critical Care, vol. 20, pp. 1-10, 2016. C. Becker, M. A. Plymale, J. Wennergren, C. Totten, K. Stigall, and J. S. Roth, "Compliance of the abdominal wall during laparoscopic insufflation," Surgical endoscopy, vol. 31, pp. 1947-1951, 2017. C. Song, A. Alijani, T. Frank, G. Hanna, and A. Cuschieri, "Mechanical properties of the human abdominal wall measured in vivo during insufflation for laparoscopic surgery," Surgical Endoscopy And Other Interventional Techniques, vol. 20, pp. 987-990, 2006. F. YC, Biomechanics : Mechanical Properties of Living Tissues. 1993. P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability," Neuroimage, vol. 31, no. 3, pp. 1116-1128, 2006. W. Wen-Fang, "Abdominal Insufflation Model for Pre-operative Surgical Planning in Minimally Invasive Surgery," Master, National Taiwan University, 2021. E. Rinninella et al., "Muscle mass, assessed at diagnosis by L3-CT scan as a prognostic marker of clinical outcomes in patients with gastric cancer: a systematic review and meta-analysis," Clinical Nutrition, vol. 39, no. 7, pp. 2045-2054, 2020. A. Gökhan, C. O. Güzeller, and M. T. Eser, "The effect of the normalization method used in different sample sizes on the success of artificial neural network model," International Journal of Assessment Tools in Education, vol. 6, no. 2, pp. 170-192, 2019. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88850 | - |
| dc.description.abstract | 在現階段的微創手術術前規劃中，仍缺少腹腔充氣後腹部表面的資訊，造成術前判斷上的困難。目前，醫師僅能透過觀察電腦斷層影像，憑藉自身經驗及判斷完成術前規劃。然而，術前電腦斷層影像與手術中腹腔充氣後表面的差異，使得醫生難以準確地進行術前規劃及開口佈局。腹腔充氣預測模型的建立能夠解決上述的困境。過去關於腹腔充氣模型的研究仍有兩個問題尚未解決。首先，過去的研究大多採用物理方法去進行腹腔形變的模擬，然而使用這些方法時需要花費大量時間去建立腹部的物理模型並計算形變。第二是過去的研究鮮少對實際應用於人體的效果進行完整的評估。本研究選擇使用深度學習的方法，透過病患的生理特徵以及術前的電腦斷層影像來預測腹腔充氣後的表面形式。
本研究一共收集20個實際接受腹腔鏡手術的病患資料。病患的生理特徵以及電腦斷層影像將作為深度學習的輸入，輸出則是病患的充氣後腹部表面。預測出的腹部膨脹表面和臨床數據比較後，平均表面誤差為6.21 mm。本研究提出了一個由深度學習方法建立腹腔膨脹預測模型的方法，能夠預測出充氣後的腹部表面。 | zh_TW |
| dc.description.abstract | Surgical planning in laparoscopic surgery is challenging because the patient’s abdominal insufflation model is unavailable preoperatively. Currently, surgeons have to conduct surgical planning based on their judgments on the computed tomography (CT) images, while these images do not show the abdominal surface after insufflation. The discrepancies between the abdominal surfaces in the CT images and surgery lead to difficulty in surgical planning, especially the port placement. Studies on abdominal insufflation prediction have emerged to solve the problem. Previous studies often adopted physics-based methods to simulate the deformation of the abdominal cavity after insufflation. However, these methods are time-consuming when constructing physical models of the abdomen and computing the deformation. Besides, these studies rarely discussed their method’s performance on human data. In this study, we adopt the machine learning method to predict the abdominal surface after insufflation using the patient’s CT images and individual characteristics.
We collect the data from 20 patients that underwent laparoscopic surgery. The patient’s individual characteristics and CT images are the input of the machine learning process, and the abdominal surface after insufflation is the output. The comparison between the model’s output and the clinical data returned an average error of 6.21 mm. This research proposed an abdominal insufflation prediction method that can predict the patient’s abdominal surface after insufflation based on a machine learning approach. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T18:02:48Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T18:02:48Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES xii Chapter 1 Introduction 1 1.1 Background 2 1.2 Problem Statements 4 1.3 Aims of the Thesis 6 1.4 Thesis Structure 8 Chapter 2 Literature Review 9 2.1 Studies of Abdominal Insufflation Simulation 10 2.2 Studies of Factors Related to Abdominal Insufflation 13 Chapter 3 Methodology 16 3.1 Method Overview 17 3.2 Abdominal Insufflation Data Collection 19 3.2.1 The ZED Camera 19 3.2.2 The Data Collection Procedure 23 3.2.3 The Surface Markers Labeling and Extraction 25 3.2.4 Coordinate Transformation 27 3.3 Data Preprocessing 30 3.3.1 The Point Cloud Generation 30 3.3.2 Surface Reconstruction 34 3.3.3 Insufflation Displacements Resampling 39 3.4 Feature Selection 43 3.4.1 Features from the Literature Review 44 3.4.2 Other Features 46 3.4.3 Class 47 3.5 Machine Learning Framework 51 3.5.1 Feature Normalization 52 3.5.2 Neural Network Architecture 53 3.5.3 Data Processing in Training 55 3.5.4 Loss Definition 56 3.5.5 Validation Method 59 3.5.6 Training Epochs 60 Chapter 4 Results and Discussion 62 4.1 Experimental Setup 63 4.2 Data Collection Results 63 4.3 Evaluation Method 64 4.4 Experiment Results 65 4.4.1 Results with Different Loss Definitions 65 4.4.2 The Predicted Surface and the Error Evaluation 67 4.4.3 The Error Distribution 76 4.5 Convolutional Neural Network 81 4.6 Sources of Error 83 4.7 Discussions 85 4.7.1 Limitations 85 4.7.2 Applications 86 Chapter 5 Conclusions 87 | - |
| dc.language.iso | en | - |
| dc.subject | 腹腔鏡手術 | zh_TW |
| dc.subject | 開口佈局 | zh_TW |
| dc.subject | 術前規劃 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Laparoscopic Surgery | en |
| dc.subject | Machine Learning | en |
| dc.subject | Port Placement | en |
| dc.subject | Surgical Planning | en |
| dc.title | 基於術前電腦斷層影像以深度學習方法預測充氣後腹部表面 | zh_TW |
| dc.title | Surface Prediction after Insufflation Based on Preoperative Computed Tomography Scan Image by Machine Learning Method | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 何明志;顏家鈺 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Chih Ho;Jia-Yush Yen | en |
| dc.subject.keyword | 腹腔鏡手術,術前規劃,開口佈局,深度學習, | zh_TW |
| dc.subject.keyword | Laparoscopic Surgery,Surgical Planning,Port Placement,Machine Learning, | en |
| dc.relation.page | 90 | - |
| dc.identifier.doi | 10.6342/NTU202303617 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| 顯示於系所單位： | 電機工程學系 | |
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