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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15432完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 董成淵(Chen-Yuan Dong) | |
| dc.contributor.author | Hsu-Cheng Huang | en |
| dc.contributor.author | 黃旭成 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:40:35Z | - |
| dc.date.copyright | 2020-07-27 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-23 | |
| dc.identifier.citation | 1. 'Number of deaths (Statistics as of 2017), World Data Lab, 2020.' 2. 'Main cause of death in Taiwan, 中華民國衛生福利部, 2020.' 3. 'Major causes of cancer deaths in Taiwan, 中華民國衛生福利部, 2020.' 4. Y. W. Chu et al., 'Selection of invasive and metastatic subpopulations from a human lung adenocarcinoma cell line,' Am J Respir Cell Mol Biol 17(3), 353-360 (1997). 5. M. Goppert-Mayer, 'Elementary file with two quantum fissures,' Ann. Phys.-Berlin 9(273-294 (1931). 6. 'R.W. Boyd, Nonlear Optics, 2008.' 7. P. A. Franken et al., 'Generation of Optical Harmonics,' Physical Review Letters 7(4), 118-119 (1961). 8. J. G. Cowpe, R. B. Longmore, and M. W. Green, 'Quantitative exfoliative cytology of normal oral squames: an age, site and sex-related survey,' J R Soc Med 78(12), 995-1004 (1985). 9. V. Hegde, 'Cytomorphometric analysis of squames from oral premalignant and malignant lesions,' Journal of Clinical and Experimental Dentistry e441-e444 (2011). 10. W. W. Sung et al., 'High nuclear/cytoplasmic ratio of Cdk1 expression predicts poor prognosis in colorectal cancer patients,' BMC Cancer 14(951 (2014). 11. C. Su Lim et al., 'Measurement of the Nucleus Area and Nucleus/Cytoplasm and Mitochondria/Nucleus Ratios in Human Colon Tissues by Dual-Colour Two-Photon Microscopy Imaging,' Sci Rep 5(18521 (2015). 12. J. L. Schmidt et al., 'Visual estimates of nucleus-to-nucleus ratios: can we trust our eyes to use the Bethesda ASCUS and LSIL size criteria?,' Cancer 114(5), 287-293 (2008). 13. L. J. Vaickus, and R. H. Tambouret, 'Young investigator challenge: The accuracy of the nuclear-to-cytoplasmic ratio estimation among trained morphologists,' Cancer Cytopathol 123(9), 524-530 (2015). 14. N. AG, 'The cell sectioning model. Nuclear/cytoplasmic ratio studied by computer simulation,' Anal Quant Cytol Histol 18(23-34 (1996). 15. K. V. Onozato ML, Yagi Y, Mino-Kenudson M. , 'A Role of Three-Dimensional (3D) Reconstruction in the Classification of Lung Adenocarcinoma,' Stud Health Technol Inform 179(250-256 (2012). 16. J. P. Thiran, and B. Macq, 'Morphological feature extraction for the classification of digital images of cancerous tissues,' IEEE Trans Biomed Eng 43(10), 1011-1020 (1996). 17. C. K. Chang et al., 'Segmentation of nucleus and cytoplasm of a single cell in three-dimensional tomogram using optical coherence tomography,' J Biomed Opt 22(3), 36003 (2017). 18. N. Pm. Pavani et al., 'Recent Advances in the Early Diagnosis of Oral Cancer: A Systematic Review,' International Journal of Medical Reviews 4(4), 119-125 (2018). 19. I. Georgakoudi et al., 'NAD(P)H and Collagen as <strong><em>in Vivo</em></strong> Quantitative Fluorescent Biomarkers of Epithelial Precancerous Changes,' Cancer Research 62(3), 682-687 (2002). 20. Y. Wu, and J. Y. Qu, 'Autofluorescence spectroscopy of epithelial tissues,' J Biomed Opt 11(5), 054023 (2006). 21. M. C. Skala et al., 'Multiphoton microscopy of endogenous fluorescence differentiates normal, precancerous, and cancerous squamous epithelial tissues,' Cancer Res 65(4), 1180-1186 (2005). 22. S. Prestin et al., 'Measurement of epithelial thickness within the oral cavity using optical coherence tomography,' Head Neck 34(12), 1777-1781 (2012). 23. I. Pavlova et al., 'Understanding the biological basis of autofluorescence imaging for oral cancer detection: high-resolution fluorescence microscopy in viable tissue,' Clin Cancer Res 14(8), 2396-2404 (2008). 24. X. Yue et al., Colorectal Cancer Outcome Prediction from H E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles (2019). 25. J. W. Wei et al., 'Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks,' Sci Rep 9(1), 3358 (2019). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15432 | - |
| dc.description.abstract | 在本文中，我們致力於開發提高癌症篩查的準確性和速度的方法，並搭建一個完整的全玻片掃描儀，以通過未來的深度學習來預測口腔癌的發展。 在第一個實驗中，我們使用程序自動計算三維核質比（核與細胞質），從而在進行病理診斷時降低了歧義。 第二組實驗我們使用醫院提供的新鮮的口腔腫瘤樣品來測試自發熒光，試圖區別正常，腫瘤和不典型增生的腫瘤部位。 第三組實驗我們與台大醫院合作，通過我們的自動掃描系統對口腔癌切片進行成像。 在未來通過與深度學習做結合，我們將來可以提高口腔癌分析的準確性。 | zh_TW |
| dc.description.abstract | In this study, we developed methods for accurate and rapid cancer screening, built a whole slide scanner for appropriate cancer diagnosis, and analyzed cancer growth via image processing. In the first experiment, we used our program to automatically calculate the three-dimensional nucleus-to-cytoplasm (N/C) ratio, thereby reducing the ambiguity during pathological diagnosis. In the second experiment, we performed autofluorescence image, on normal tissue, tumor, and dysplasia specimens. In the third experiment, we performed whole-slide imaging on histological sections of oral cancer slides using automatic scanning system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:40:35Z (GMT). No. of bitstreams: 1 U0001-2307202015514100.pdf: 3701108 bytes, checksum: 54dc8a1760769b09ca7d56f1754eac64 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii Abstract iii CONTENTS v LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Motivation………………….. 1 Chapter 2 Instruments and setup 4 2.1 Multiphoton microscope 4 2.1.1 Mechanism of two-photon absorption 5 2.2 Optical resolution 7 2.3 Second Harmonic Generation 8 Chapter 3 Three-dimensional nucleus-to cytoplasm ratios 11 3.1 Introduction 11 3.2 Materials and methods 12 3.2.1 Multiphoton microscope 12 3.2.2 Preparation of normal and lung adenocarcinoma cell lines 13 3.2.3 Image processing 14 3.3 Results 15 3.4 Discussion 18 Chapter 4 Auto-fluorescence imaging of human oral cancer tumor 20 4.1 Introduction 20 4.1.1 Autofluorescence in tissue 20 4.2 Materials and methods 20 4.2.1 Sample preparation 20 4.3 Results 21 4.4 Discussion 26 Chapter 5 Whole slide imaging of human oral cancer H E specimens 28 5.1 Introduction 28 5.2 Materials and methods 28 5.2.1 Whole slide scanner 28 5.3 Results 29 5.4 Discussion 35 Chapter 6 Conclusions 36 References 37 | |
| dc.language.iso | en | |
| dc.subject | 影像分析 | zh_TW |
| dc.subject | 雙光子激發 | zh_TW |
| dc.subject | 光學顯微術 | zh_TW |
| dc.subject | 口腔癌 | zh_TW |
| dc.subject | 癌症檢測 | zh_TW |
| dc.subject | tumor diagnosis | en |
| dc.subject | image processing | en |
| dc.subject | two-photon excitation | en |
| dc.subject | optical microscope | en |
| dc.subject | oral cancer | en |
| dc.title | 光學顯微鏡在癌症診斷中的應用 | zh_TW |
| dc.title | Applications of optical microscopy in cancer diagnostics | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳政維(Jeng-Wei Chen),王立民(Li-Min Wang),陳永芳(Yang-Fang Chen),婁培人(Pei-Jen Lou),林玫君(Mei-Chun Lin) | |
| dc.subject.keyword | 雙光子激發,光學顯微術,口腔癌,癌症檢測,影像分析, | zh_TW |
| dc.subject.keyword | two-photon excitation,optical microscope,oral cancer,tumor diagnosis,image processing, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202001785 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-07-24 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 應用物理研究所 | zh_TW |
| 顯示於系所單位： | 應用物理研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2307202015514100.pdf 未授權公開取用 | 3.61 MB | Adobe PDF |
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