請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72286
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張恆華 | |
dc.contributor.author | Hsiao-Fu Kuo | en |
dc.contributor.author | 郭曉芙 | zh_TW |
dc.date.accessioned | 2021-06-17T06:33:21Z | - |
dc.date.available | 2023-08-18 | |
dc.date.copyright | 2018-08-18 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-16 | |
dc.identifier.citation | [1] W. H. Organization, 'The top 10 causes of death,' 2018.
[2] R. J. Mural, M. D. Adams, E. W. Myers, H. O. Smith, G. L. G. Miklos, R. Wides, et al., 'A Comparison of Whole-Genome Shotgun-Derived Mouse Chromosome 16 and the Human Genome,' Science, vol. 296, p. 1661, 2002. [3] B. A. Moffat, C. J. Galbán, and A. Rehemtulla, 'Advanced MRI: Translation from Animal to Human in Brain Tumor Research,' Neuroimaging Clinics of North America, vol. 19, pp. 517-526, 2009/11/01/ 2009. [4] N. D. E. Greene, M. F. Lythgoe, D. L. Thomas, R. L. Nussbaum, D. J. Bernard, and H. M. Mitchison, 'High resolution MRI reveals global changes in brains of Cln3 mutant mice,' European Journal of Paediatric Neurology, vol. 5, pp. 103-107, 2001/01/01/ 2001. [5] O. Natt, T. Watanabe, S. Boretius, J. Radulovic, J. Frahm, and T. Michaelis, 'High-resolution 3D MRI of mouse brain reveals small cerebral structures in vivo,' Journal of Neuroscience Methods, vol. 120, pp. 203-209, 2002/10/30/ 2002. [6] J. Zhang, Q. Peng, Q. Li, N. Jahanshad, Z. Hou, M. Jiang, et al., 'Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images,' NeuroImage, vol. 49, pp. 2340-2351, 2010/02/01/ 2010. [7] S. Millman, I. I. Rabi, and J. R. Zacharias, 'On the Nuclear Moments of Indium,' Physical Review, vol. 53, pp. 384-391, 03/01/ 1938. [8] Y. Zhang, B. J. Matuszewski, and L. K. Shark, 'A Novel Medical Image Segmentation Method using Dynamic Programming,' in International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007), 2007, pp. 69-74. [9] P. L. Chang and W. G. Teng, 'Exploiting the Self-Organizing Map for Medical Image Segmentation,' in Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007, pp. 281-288. [10] K. O. Lim and A. Pfefferbaum, 'Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter,' Journal of Computer Assisted Tomography, vol. 13, pp. 588-593, 1989. [11] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, 'Magnetic resonance image tissue classification using a partial volume model,' NeuroImage, vol. 13, pp. 856-876, 2001. [12] S. M. Smith, 'Fast robust automated brain extraction,' Human brain mapping, vol. 17, pp. 143-155, 2002. [13] F. N. Christine, O. I. Burak, C. C. P., M. Shaunna, B. G. Amanda, B. M. W., et al., 'Quantitative evaluation of automated skull‐stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location,' Human Brain Mapping, vol. 27, pp. 99-113, 2006. [14] M. Murugavel and J. M. Sullivan, 'Automatic cropping of MRI rat brain volumes using pulse coupled neural networks,' NeuroImage, vol. 45, pp. 845-854, 2009/04/15/ 2009. [15] J. Wang, C. Vachet, A. Rumple, S. Gouttard, C. Ouziel, E. Perrot, et al., 'Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline,' Frontiers in neuroinformatics, vol. 8, 2014. [16] H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, et al., 'Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,' IEEE Transactions on Medical Imaging, vol. 35, pp. 1285-1298, 2016. [17] W. Zhang, R. Li, H. Deng, L. Wang, W. Lin, S. Ji, et al., 'Deep convolutional neural networks for multi-modality isointense infant brain image segmentation,' Neuroimage, vol. 108, pp. 214-24, Mar 2015. [18] N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, et al., 'Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,' IEEE Transactions on Medical Imaging, vol. 35, pp. 1299-1312, 2016. [19] K. He, X. Zhang, S. Ren, and J. Sun, 'Deep Residual Learning for Image Recognition,' Proc. Int. Conf. Learn, 2015. [20] E. Dumitru, M. Pierre-Antoine, B. Yoshua, B. Samy, and V. Pascal, 'The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training,' 2009. [21] C. Westbrook and C. K. Roth, MRI in Practice: Wiley-Blackwell, 2011. [22] R. H. Hashemi, W. G. Bradley, and C. J. Lisanti, MRI: the basics: LWW, 2012. [23] (2013, 6/10). 磁振造影設備. Available: http://www.ntdtv.com/xtr/b5/2013/02/27/a854311.html [24] Y. H. Wang and T. L. Fu., 'Research on segmentation methods of brain using mri images.,' 2011 International Conference on Energy and Environmental Science, pp. 2382--2388, 2011. [25] R. Stokking, K. L. Vincken, and M. A. Viergever, 'Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data,' NeuroImage, vol. 12, pp. 726-738, 2000. [26] T. F. Chan and L. A. Vese, 'Active contours without edges,' IEEE Transactions on Image Processing, vol. 10, pp. 266-277, 2001. [27] J. L. McClelland, D. E. Rumelhart, and P. R. Group, 'Parallel distributed processing,' Explorations in the microstructure of cognition, vol. 2, 1986. [28] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, 'Learning representations by back-propagating errors,' in Neurocomputing: foundations of research, A. A. James and R. Edward, Eds., ed: MIT Press, 1988, pp. 696-699. [29] S. Lawrence, C. L. Giles, T. Ah Chung, and A. D. Back, 'Face recognition: a convolutional neural-network approach,' IEEE Transactions on Neural Networks, vol. 8, pp. 98-113, 1997. [30] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, 'Gradient-based learning applied to document recognition,' Proceedings of the IEEE, vol. 86, pp. 2278-2324, 1998. [31] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'ImageNet classification with deep convolutional neural networks,' presented at the Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Lake Tahoe, Nevada, 2012. [32] A. Dertat, 'Applied Deep Learning - Part 4: Convolutional Neural Networks.' [33] W. Gomez, W. C. A. Pereira, and A. F. C. Infantosi, 'Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound,' IEEE Transactions on Medical Imaging, vol. 31, pp. 1889-1899, 2012. [34] J. Long, E. Shelhamer, and T. Darrell, 'Fully convolutional networks for semantic segmentation,' in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431-3440. [35] C. Szegedy, L. Wei, J. Yangqing, P. Sermanet, S. Reed, D. Anguelov, et al., 'Going deeper with convolutions,' in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1-9. [36] K. He, X. Zhang, S. Ren, and J. Sun, 'Identity Mappings in Deep Residual Networks,' ed, 2016. [37] S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015. [38] O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional Networks for Biomedical Image Segmentation,' 2015, pp. 234-241. [39] L. R. Dice, 'Measures of the amount of ecologic association between species,' Ecology, vol. 26, pp. 297-302, 1945. [40] D. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, 2014. [41] K. He, X. Zhang, S. Ren, and J. Sun, 'Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,' presented at the Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), 2015. [42] C. Nvidia, 'Tesla K40 GPU Accelerator Overview,' 2014. [43] H.-H. Chang, A. H. Zhuang, D. J. Valentino, and W.-C. Chu, 'Performance measure characterization for evaluating neuroimage segmentation algorithms,' Neuroimage, vol. 47, pp. 122-135, 2009. [44] P. Jaccard, 'THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE,' New Phytologist, vol. 11, pp. 37-50, 1912. [45] N. Otsu, 'A Threshold Selection Method from Gray-Level Histograms,' IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, pp. 62-66, 1979. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72286 | - |
dc.description.abstract | 腦中風是世界致死率第二高的疾病,主要可以分為缺血型與出血型兩大類,在臨床上有許多研究發展空間。而臨床前實驗模型中,多半使用齧齒動物輔以磁振影像作為實驗研究依據。研究者在分析作業前需要經過許多的處理步驟,例如將大腦區域與缺血型中風區域提取出來,然而這些區域不僅需要專家耗時且費力的手動分割外,也亦產生標準不一的問題。因此,本篇論文的主要目的是希望能藉由全卷積神經網路來達成全自動化的分割預測。為了使神經網路能夠正確分割出中風區域,我們將分割步驟分為兩個階段:大腦分割及缺血型中風區域分割,兩者皆使用相同的卷積神經網路分割系統。透過編譯-反編譯的網路架構與結合不同階層特徵的方式,我們能夠較準確地找到需要的特徵並以逐像素(pixel-wise)的方式判別。最後,再利用一些簡單的形態學處理優化分割出的影像。本篇研究中使用了35筆老鼠中風資料,實驗結果顯示本研究方法可以相當準確地截取老鼠大腦(98.12%),對於中風區域亦有優良的分割結果(80.47%)。 | zh_TW |
dc.description.abstract | Stroke has the second highest fatality rate around the world. It can be divided into two major categories: ischemic and hemorrhagic. In the clinically, there is lots of development prospect. Rodents associated with magnetic resonance (MR) images are often preclinical experimental models. Researchers need to go through many processing steps before analyzing the operation, such as extracting the brain regions and infarct regions. However, these regions need lots of time for manual segmentation by experts, who may have the problems of different standards. Therefore, the main purpose of thesis is to achieve fully automated segmentation by using fully convolutional neural networks. In order to segment the right region, we divide the segmentation step into two phases: brain segmentation and infarct segmentation, both of which using the same convolutional neural network. With an encoder-decoder network and concatenating different levels of features, we are able to find the accurate features that we need and classify them pixel by pixel. Finally, some simple morphological methods are applied to optimize the segmentation results. In this study, 35 rat brain MR image data were used. The experimental results show that the proposed method accurately extracted the rat brain (98.12%) and provided good segmentation results for the infarct region (80.47%). | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:33:21Z (GMT). No. of bitstreams: 1 ntu-107-R05525067-1.pdf: 3676958 bytes, checksum: bad9628c43c25b1106ba807abaf7a3d7 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 ix 符號表 x 第 1 章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 論文架構 4 第 2 章 文獻探討 5 2.1 磁振造影介紹 5 2.2 腦部分割演算法 6 2.2.1 大腦擷取工具 6 2.3 神經網路 7 2.3.1 神經元 8 2.3.2 梯度下降 11 2.3.3 參數說明 12 2.4 神經網路訓練 13 2.4.1 前饋 14 2.4.2 倒傳遞 15 2.5 卷積神經網路 16 2.5.1 卷積層 16 2.5.2 池化層 19 2.5.3 全連接層 20 第 3 章 研究設計與方法 21 3.1 方法流程 22 3.2 影像預處理 22 3.3 資料增強 24 3.4 卷積神經網路模型 25 3.4.1 全卷積網路 25 3.4.2 殘差網路 26 3.4.3 批量正規化 28 3.4.4 網路架構 29 3.5 預測區域最佳化 34 第 4 章 實驗結果及討論 35 4.1 實驗說明 35 4.1.1 實驗環境 36 4.1.2 資料集 37 4.1.3 評估標準 38 4.2 網路模型判別及預測能力之評估 40 4.3 去腦殼分割結果 42 4.3.1 卷積神經網路模型之預測與評估 42 4.3.2 優化後的影像分析 47 4.3.3 其他方法比較 50 4.4 缺血型中風區域分割結果 52 4.4.1 卷積神經網路模型之預測與評估 52 4.4.2 其他方法比較 59 第 5 章 結論及未來展望 62 5.1 結論 62 5.2 未來展望 63 參考文獻 64 | |
dc.language.iso | zh-TW | |
dc.title | 以卷積神經網路為基礎的老鼠腦部磁振影像中風區域分割之研究 | zh_TW |
dc.title | Infarct Region Segmentation in Rat Brain MR Images after Stroke Based on Convolutional Neural Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張瑞益,丁肇隆,江明彰,葉馨喬 | |
dc.subject.keyword | 磁振影像,缺血型中風,影像分割,卷積神經網路,編譯-反編譯, | zh_TW |
dc.subject.keyword | magnetic resonance image(MRI),ischemic stroke,image segmentation,convolutional neural network,encoder-decoder, | en |
dc.relation.page | 67 | |
dc.identifier.doi | 10.6342/NTU201803479 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-16 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 3.59 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。