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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80904完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 施庭芳(Tiffany Ting-Fang Shih) | |
| dc.contributor.author | Chih-Horng Wu | en |
| dc.contributor.author | 吳志宏 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:21:18Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:21:18Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-23 | |
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Rong Y-D, Bian A-L, Hu H-Y, Ma Y, Zhou X-Z. Study on relationship between elderly sarcopenia and inflammatory cytokine IL-6, anti-inflammatory cytokine IL-10. BMC Geriatrics. 2018 Dec 12;18(1):308. 132. Arbanas J, Starcevic Klasan G, Nikolic M, Jerkovic R, Miljanovic I, Malnar D. Fibre type composition of the human psoas major muscle with regard to the level of its origin. J Anat. 2009 Dec;215(6):636–41. 133. Wakabayashi H, Watanabe N, Anraku M, Oritsu H, Shimizu Y. Pre-operative psoas muscle mass and post-operative gait speed following total hip arthroplasty for osteoarthritis. J Cachexia Sarcopenia Muscle. 2016 Mar;7(1):95–6. 134. Park J, Gil JR, Shin Y, Won SE, Huh J, You M-W, et al. Reliable and robust method for abdominal muscle mass quantification using CT/MRI: An explorative study in healthy subjects. PLoS One [Internet]. 2019 Sep 19 [cited 2021 Apr 29];14(9). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752777/ 135. Kim JC, Lee S-U, Jung SH, Lim J-Y, Kim DH, Lee SY. Natural aging course of paraspinal muscle and back extensor strength in community-dwelling older adults (sarcopenia of spine, SarcoSpine): a prospective cohort study protocol. BMJ Open. 2019 Sep 1;9(9):e032443. 136. Khan AI, Reiter DA, Sekhar A, Sharma P, Safdar NM, Patil DH, et al. MRI quantitation of abdominal skeletal muscle correlates with CT-based analysis: implications for sarcopenia measurement. Appl Physiol Nutr Metab. 2019 Aug;44(8):814–9. 137. Desmeules S, Lévesque R, Jaussent I, Leray-Moragues H, Chalabi L, Canaud B. Creatinine index and lean body mass are excellent predictors of long-term survival in haemodiafiltration patients. Nephrol Dial Transplant. 2004 May;19(5):1182–9. 138. Gomez-Perez SL, Haus JM, Sheean P, Patel B, Mar W, Chaudhry V, et al. Measuring Abdominal Circumference and Skeletal Muscle From a Single Cross-Sectional Computed Tomography Image: A Step-by-Step Guide for Clinicians Using National Institutes of Health ImageJ. JPEN J Parenter Enteral Nutr. 2016 Mar;40(3):308–18. 139. Edwards K, Chhabra A, Dormer JD, Jones P, Boutin RD, Lenchik L, et al. Abdominal muscle segmentation from CT using a convolutional neural network. In: Gimi BS, Krol A, editors. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging [Internet]. Houston, United States: SPIE; 2020 [cited 2020 Aug 17]. p. 20. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11317/2549406/Abdominal-muscle-segmentation-from-CT-using-a-convolutional-neural-network/10.1117/12.2549406.full 140. Giusto M, Lattanzi B, Albanese C, Galtieri A, Farcomeni A, Giannelli V, et al. Sarcopenia in liver cirrhosis: the role of computed tomography scan for the assessment of muscle mass compared with dual-energy X-ray absorptiometry and anthropometry. Eur J Gastroenterol Hepatol. 2015 Mar;27(3):328–34. 141. Demerath EW, Shen W, Lee M, Choh AC, Czerwinski SA, Siervogel RM, et al. Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr. 2007 Feb;85(2):362–8. 142. Brennan DD, Whelan PF, Robinson K, Ghita O, O’Brien JM, Sadleir R, et al. Rapid automated measurement of body fat distribution from whole-body MRI. AJR Am J Roentgenol. 2005 Aug;185(2):418–23. 143. Addeman BT, Kutty S, Perkins TG, Soliman AS, Wiens CN, McCurdy CM, et al. Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method. J Magn Reson Imaging. 2015 Jan;41(1):233–41. 144. B G, Jm P, R L, S A, D BS, D M, et al. Liver methylene fraction by dual- and t……… | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80904 | - |
| dc.description.abstract | "緒論 肌少症一開始主要用於描述老人生理狀況逐漸退化，依照歐洲老年肌少症工作小組定義肌少症包括肌肉量減少、肌肉力氣降低及肌肉表現功能下降。次發性的肌少症和許多慢性疾病有關，包括癌症、肝硬化、慢性腎臟病等等。在過去，營養狀況常以身體質量指數、上臂中圍、血清中白蛋白或生物阻抗分析等來代表。但這些臨床指標在嚴重癌症、肝硬化及慢性腎臟病時會因為皮下水腫及腹水產生的高估的情形。相對來說，斷面影像檢查如電腦斷層及磁振造影，可以對腹部的肌肉及脂肪作準確的分析。腹部肌肉還可分類為腰大肌、豎脊肌、腹壁肌及腹直肌等等。而對於脂肪組織可以分類為臟器脂肪及皮下脂肪。由以上可知使用影像方法來進行肌肉量的計算在肌少症的臨床應用上清楚而客觀。 目前在肌少症的研究有三個未解的問題，一是對肌少症的診斷標準及肌肉量下降的閾值並沒有一致的共識，因此大型及多樣性針對不同性別、種族及疾病族群的肌少症相關研究有其必要性。尤其是早期肌少症的標準由歐洲老年肌少症工作小組所訂，其研究的族群以西方國家為主，西方人的身體組成與東方人有明顯差異，東方國家的研究以日本為主，而且大量使用電腦斷層作為測量腹部肌肉及腰大肌的工具。二是使用斷面影像測量腹部肌肉量的過程對醫師是一項耗時費力的工作，近年來由於機器學習及深度學習在圖形辨識領域的發展，快速且精準的自動切割腹部肌肉及脂肪組織的影像後處理工具變成可能。三是根據以往的經驗及研究，病人的肌少症很難藉由營養的補充或藥物達成，對於生重病的病人使用高強度的鍛鍊也不切實際。因此能否用微創手術的方法來增加食慾並進一步改善病人的肌少症便成為值得探索的話題。 方法 我們以臺大醫院的影像資料庫為材料，收集胰臟癌、晚期肝癌及腹膜透析病人。對胰臟癌的病人我們分別使用西方(俄羅斯研究單位)及東方(臺大醫院)的診斷標準來評估是否為預後的重要因子；對晚期肝癌的病人，我們分別測量腹部肌肉的腰大肌、豎脊肌、腹壁肌及腹直肌，以研究何者為影響存活率的因子；對接受腹膜透析的病人，我們除了測量腹部肌肉及腰大肌的肌肉量，並與淨軟組織量做相關性分析，找出何者較具代表性。然後我們結合前面三個資料集所收集的影像，利用我們已經圈選的遮罩進行機器學習及深度學習訓練自動切割肌肉及脂肪組織的模型。並利用遷移學習的方式將電腦斷層所建立的模型參數轉到磁振造影使用，然後評估兩者的相關性。最後我們利用經頸靜脈肝內門體靜脈支架分流術來治療因肝硬化有靜脈瘤出血及腹水的病人及腎動脈栓塞術來治療多囊腎的病人，並比較治療前及六個月後的影像，能否也同時改善病人肌少症的狀況。 結果 第一部分：經過在俄羅斯的研究單位及臺大醫院對胰臟癌病人進行影像分析後發現對於腹部肌肉、臟器脂肪及皮下脂肪的測量為高度相關(r = 0.974, 0.978, 0.979, p < 0.001)。 但在存活分析中，依照東方的診斷標準才是顯著的存活因子(p = 0.008)，西方的診斷標準不是(p = 0.807)。在對晚期肝癌病人進行個別肌肉測量後顯示對於腹部肌肉，僅腰大肌是顯著影響存活率的因子(p = 0.014)，豎脊肌、腹壁肌及腹直肌不是。在腹膜透析病人進行淨體重估計及腹部影像分析後發現，腰大肌比起全腹部肌肉的測量與淨軟組織量有較高的相關性(r = 0.775 vs. 0.681, p < 0.001)，多因子分析顯示腰大肌是顯著影響全存活率的因子(HR: 2.386, CI: 1.315-4.330)。 第二部分：使用機器學習及深度學習演算法與醫師圈選範圍進行相關性分析，Sobel邊界偵測、AlexNet、VGG、ResNet與基準真相的相關係數分別為0.85, 0.81, 0.91, 0.78 (p < 0.05)。若以VGG的演算法來區分個別腹部肌肉，與豎脊肌、腰大肌、腹壁肌及腹直肌的相關係數分別為(r = 0.85, 0.43, 0.42, 0.02)。接著我們把利用電腦斷層建立的深度模型參數取出，利用遷移學習來對有腹部磁振造影影像的病人進行腹部肌肉自動切割，其精確度相對於隨機參數分別為0.9704及0.9696。最後我們分析使用電腦斷層及磁振造測量腹部肌肉、臟器脂肪及皮下脂肪為高度相關(r = 0.946, 0.969, 0.953)。 第三部分：使用經頸靜脈肝內門體靜脈支架分流術來治療因肝硬化相關的併發症，治療後六個月測量影像上腰大肌的面積明顯大於治療前(15.66±6.99 vs. 13.11±5.47, p = 0.040)，病人的食慾變好。使用腎動脈栓塞術來治療多囊腎，比較治療前及治療後六個月的影像，腎臟的體積明顯縮小(2979.8±2069.6 vs. 4019.4±2438.2, p < 0.001)，腰大肌的面積明顯增大(13.6±7.7 vs. 11.6±6.7, p < 0.001)，病人的腹脹症狀也明顯改善。 結論 肌少症的診斷標準需要依照性別、族群被正確的選擇才能做出正確的診斷。在腹部肌肉當中，腰大肌肌肉量是最重要的預後因子，也是最具代表性的肌肉。對電腦斷層及磁振造影影像，利用深度學習進行肌肉自動切割的正確性高且兩者的值也具一致性。我們可以利用介入放射的方法來治療病人的原始疾病，並有機會同時改善肌少症。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:21:18Z (GMT). No. of bitstreams: 1 U0001-1909202121470900.pdf: 6399537 bytes, checksum: ee3a3f5287b1e2830b7bb65777ff5afa (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "誌謝 ii 中文摘要 iii ABSTRACT vi CONTENTS ix INDEX OF FIGURES xiv INDEX OF TABLES xvi ABBREVIATIONS xviii Chapter 1 Introduction 1 1.1 Sarcopenia in patients with hepato-biliary-pancreatic and renal disease 1 1.1.1 Sarcopenia in patients with pancreatic cancer 2 1.1.2 Sarcopenia in patients with advanced hepatocellular carcinoma 2 1.1.3 Sarcopenia in patients receiving peritoneal dialysis 2 1.2 Imaging diagnosis of sarcopenia 3 1.2.1 Measurement of the abdominal muscle mass with CT 3 1.2.2 Diagnostic criteria for sarcopenia and cut-off values of muscle index 4 1.2.3 Stratification of the abdominal muscle mass with CT 5 1.2.4 Correlation between CT-based abdominal muscle mass assessment with lean soft tissue 5 1.3 Machine and Deep Learning Algorithms for Muscle Segmentation 7 1.3.1 Canny edge detection in image analysis 8 1.3.2 Convolution neural network in image analysis 10 1.3.3 Convolution neural network plus edge detection in image analysis 11 1.3.4 Transfer learning from CT to DIXON-MRI images 11 1.3.5 Correlation between non-enhanced CT and PDFF-MRI images 13 1.4 Interventional procedures to improve sarcopenia 13 1.4.1 Transjugular intrahepatic portosystemic shunt affects muscle mass in patients with liver cirrhosis 14 1.4.2 Renal artery embolization increases muscle mass in patients with polycystic kidney disease. 16 Chapter 2 Materials and Methods 20 2.1 Image datasets 20 2.1.1 Image dataset from patients with pancreatic cancer 20 2.1.2 Image dataset from patients with advanced HCC 21 2.1.3 Image dataset from patients receiving peritoneal dialysis 21 2.2 Preprocessing and Equipment 22 2.2.1 Images preprocessing via Image J 22 2.2.2 Equipment for machine learning and deep learning 23 2.3 Modeling 23 2.3.1 Gaussian smoothing and gradient image processing 23 2.3.2 Convolutional neural network structure 24 2.3.3 AlexNet 26 2.3.4 Visual Geometry Group (VGG) 27 2.3.5 GoogLeNet (Inception) 27 2.3.6 Residual neural network (ResNet) 28 2.3.7 Fully convolutional network (FCN) 28 2.4 Transfer learning 29 2.4.1 Background of transfer learning 29 2.4.2 Imaging processing and modeling of transfer learning 30 2.5 Body composition analysis of CT and PDFF-MRI 31 2.6 Interventional procedures 33 2.6.1 Muscle change after TIPS in patients with liver cirrhosis 33 2.6.2 Muscle change after renal artery embolization in patients with polycystic kidney disease 37 2.7 Statistical analysis 39 2.7.1 Statistical method for patients with pancreatic cancer 39 2.7.2 Statistical method for patients with advanced HCC 40 2.7.3 Statistical method for patients receiving peritoneal dialysis 41 2.7.4 Statistical method for computational medicine 41 2.7.5 Statistical method for correlation between CT and PDFF-MRI 42 2.7.6 Statistical method for the minimally invasive procedure 43 Chapter 3 Results 44 3.1 Results of diagnostic criteria and measurement algorithms establishment 44 3.1.1 Comparing Western and Eastern criteria for sarcopenia and their association with survival in patients with pancreatic cancer 44 3.1.2 Total skeletal, psoas, and rectus abdominis muscle mass as prognostic factors for patients with advanced HCC 47 3.1.3 Computed tomography-based sarcopenia in patients receiving peritoneal dialysis: Correlation with lean soft tissue and survival 51 3.2 Results of semantic segmentation of abdominal muscle using deep learning and transfer leaning 54 3.2.1 Correlation of muscle segmentation results with different machine learning and deep learning algorithms 54 3.2.2 Transferring learning from CT to DIXON-MRI 55 3.2.3 Correlation of body composition between CT and PDFF-MRI 55 3.3 Results of interventional procedures to improve sarcopenia 57 3.3.1 Results of muscle gain after TIPS in decompensated liver cirrhosis 57 3.3.2 Results of renal artery embolization to increase muscle mass in polycystic kidney disease 58 Chapter 4 Discussion 59 4.1 Discussion of diagnostic criteria and measurement algorithms 59 4.1.1 Discussion of comparing Western and Eastern criteria for sarcopenia in patients with pancreatic cancer 59 4.1.2 Discussion of specific muscle mass as prognostic factors for patients with advanced HCC 62 4.1.3 Discussion of correlation between CT images and lean soft tissue and their impact on survival 64 4.2 Discussion semantic segmentation of abdominal muscle using deep learning and transfer learning 69 4.2.1 Discussion of muscle segmentation with different machine learning and deep learning algorithms 69 4.2.2 Discussion of transferring learning from CT to DIXON-MRI 70 4.2.3 Discussion of body composition analysis between CT and PDFF-MRI 70 4.3 Discussion of interventional procedures to improve sarcopenia 72 4.3.1 Discussion of TIPS to increase muscle mass in cirrhotic patients 72 4.3.2 Discussion of renal artery embolization to increase muscle mass in polycystic kidney disease 73 Chapter 5 Perspectives 75 5.1 Future Work 75 5.1.1 Establishment of diagnostic criteria for sarcopenia in the Taiwanese population 75 5.1.2 Development of automatic segmentation program for CT and MRI images for body composition analysis 75 5.1.3 Proton magnetic resonance spectroscopy and PDFF-MRI to analyze intramyocellular lipid droplets and intermuscular adipose tissue 76 5.1.4 Serum biomarkers or muscle biopsy to explore the causal relationship of sarcopenia 76 5.2 The possible problems for future work 77 5.3 Future study directions 77 REFERENCES 79 FIGURES 92 TABLES 120 APPENDIX 140" | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 肝細胞癌 | zh_TW |
| dc.subject | 胰臟癌 | zh_TW |
| dc.subject | 肌少症 | zh_TW |
| dc.subject | 磁振造影 | zh_TW |
| dc.subject | 電腦斷層 | zh_TW |
| dc.subject | 腎衰竭 | zh_TW |
| dc.subject | Hepatocellular carcinoma | en |
| dc.subject | Pancreatic cancer | en |
| dc.subject | Sarcopenia | en |
| dc.subject | Magnetic resonance imaging | en |
| dc.subject | Computed tomography | en |
| dc.subject | Renal failure | en |
| dc.subject | Deep learning | en |
| dc.title | 以影像為基礎的身體組成分析於肌少症病人的診斷、自動定量及治療反應評估 | zh_TW |
| dc.title | "Image-Based Body Composition Analysis for the Diagnosis, Automatic Quantification and Treatment Response Evaluation in Patients with Sarcopenia " | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0002-7498-4183 | |
| dc.contributor.advisor-orcid | 施庭芳(0000-0002-3292-9688) | |
| dc.contributor.coadvisor | 何明志(Ming-Chih Ho),高嘉宏(Jia-Horng Kao) | |
| dc.contributor.coadvisor-orcid | 何明志(0000-0003-3660-1062),高嘉宏(0000-0002-2442-7952) | |
| dc.contributor.oralexamcommittee | 黃凱文(Hsin-Tsai Liu),陳文翔(Chih-Yang Tseng),許駿,周嘉揚,陳慰宗 | |
| dc.subject.keyword | 電腦斷層,磁振造影,肌少症,胰臟癌,肝細胞癌,腎衰竭,深度學習, | zh_TW |
| dc.subject.keyword | Computed tomography,Magnetic resonance imaging,Sarcopenia,Pancreatic cancer,Hepatocellular carcinoma,Renal failure,Deep learning, | en |
| dc.relation.page | 140 | |
| dc.identifier.doi | 10.6342/NTU202103252 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-23 | |
| dc.contributor.author-college | 醫學院 | zh_TW |
| dc.contributor.author-dept | 臨床醫學研究所 | zh_TW |
| 顯示於系所單位： | 臨床醫學研究所 | |
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