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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松 | |
| dc.contributor.author | Bo-Ren Xiao | en |
| dc.contributor.author | 蕭博仁 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:29:17Z | - |
| dc.date.available | 2020-03-11 | |
| dc.date.copyright | 2020-03-11 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-03-02 | |
| dc.identifier.citation | J. Ferlay, H. R. Shin, F. Bray, D. Forman, C. Mathers, and D. M. Parkin, 'Estimates of Worldwide Burden of Cancer in 2008: Globocan 2008,' International Journal of Cancer, vol. 127, no. 12, pp. 2893-2917, December 2010.
Y. A. Ghouri, I. Mian, and J. H. Rowe, 'Review of Hepatocellular Carcinoma: Epidemiology, Etiology, and Carcinogenesis,' Journal of Carcinogenesis, vol. 16, no. 1, pp. 1-1, May 2017. N. M. Tunissiolli, M. M. U. Castanhole-Nunes, P. M. Biselli-Chicote, E. C. Pavarino, R. F. da Silva, R. C. da Silva, and E. M. Goloni-Bertollo, 'Hepatocellular Carcinoma: a Comprehensive Review of Biomarkers, Clinical Aspects, and Therapy,' Asian Pacific journal of cancer prevention, vol. 18, no. 4, pp. 863-872, April 2017. T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, 'The Use of Active Shape Models for Locating Structures in Medical Images,' Image and Vision Computing, vol. 12, no. 6, pp. 355–365, July-August 1994. T. Heimann, H.-P. Meinzer, and I. Wolf, 'A Statistical Deformable Model for the Segmentation of Liver CT Volumes,' in Proc. MICCAI Workshop 3-D Segmentation Clinic: A Grand Challenge, Brisbane, Australia, pp. 161–166, January 2007. S. J. P and Q. Yang, 'A Survey on Transfer Learning,' IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, October 2010. M. Bellver, K.-K. Maninis, J. Pont-Tuset, X. Giro-I-Nieto, J. Torres, and L. Van Gool, 'Detection-Aided Liver Lesion Segmentation Using Deep Learning,' arXiv:1711.11069 [cs.CV], November 2017. H. Xiao, 'Automatic Liver Lesion Segmentation Using a Deep Convolutional Neural Network Method,' arXiv:1704.07239 [cs.CV], April 2017. T. Heimann et al., 'Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets,' IEEE Transactions on Medical Imaging, vol. 28, no. 8, pp. 1251-1265, August 2009. K. A. Saddi, M. Rousson, C. Chefd’hotel, and F. Cheriet, 'Global-to-Local Shape Matching for Liver Segmentation in CT Imaging,' in Proc. MICCAI Workshop 3-D Segmentation Clinic: A Grand Challenge, Brisbane, Australia, pp. 207–214, January 2007. L. Ruskó, G. Bekes, G. Németh, and M. Fidrich, 'Fully Automatic Liver Segmentation for Contrast-Enhanced CT Images,' in Proc. MICCAI Workshop 3-D Segmentation. Clinic: A Grand Challenge, Brisbane, Australia, pp. 143–150, November 2007. D. Wong, J. Liu, Y. Fengshou, Q. Tian, W. Xiong, J. Zhou, Y. Qi, T. Han, S. Venkatesh, and S.-C. Wang, 'A Semi-Automated Method for Liver Tumor Segmentation Based on 2D Region Growing with Knowledge-Based Constraints,' in Proc. MICCAI workshop on 3D segmentation in the clinic: A grand challenge II, New York, USA, pp.159, January 2008. D. Furukawa, A. Shimizu, and H. Kobatake, 'Automatic Liver Segmentation Based on Maximum a Posterior Probability Estimation and Level Set Method,' in Proc. MICCAI Workshop 3-D Segmentation Clinic: A Grand Challenge, Brisbane, Australia, pp. 117–124, January 2007. E. van Rikxoort, Y. Arzhaeva, and B. van Ginneken, 'Automatic Segmentation of the Liver in Computed Tomography Scans with Voxel Classification and Atlas Matching,' in Proc. MICCAI Workshop 3-D Segmentation. Clinic: A Grand Challenge, Brisbane, Australia, pp. 101–108, January 2007. C.-L. Kuo, S.-C. Cheng, C.-L. Lin, K.-F. Hsiao, and S.-H. Lee, 'Texture-based Treatment Prediction by Automatic Liver Tumor Segmentation on Computed Tomography,' in 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), Dalian, China, pp. 128-132, July 2017. 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, November 1998. K. Lenc and A. Vedaldi, 'Understanding Image Representations by Measuring Their Equivariance and Equivalence,' in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 991-999, June 2015. J. Long, E. Shelhamer, and T. Darrell, 'Fully Convolutional Networks for Semantic Segmentation,' in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 3431-3440, June 2015. H. Noh, S. Hong, and B. Han, 'Learning Deconvolution Network for Semantic Segmentation,' in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1520-1528, December 2015. V. Badrinarayanan, A. Kendall, and R. Cipolla, 'SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481-2495, December 2017. O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional Networks for Biomedical Image Segmentation,' in Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, pp. 234-241, May 2015. N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, 'Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,' IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299-1312, May 2016. A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'ImageNet Classification with Deep Convolutional Neural Networks', In NIPS, Nevada, USA, pp. 1097-1105, December 2012. K. Simonyan and A. Zisserman. 'Very Deep Convolutional Networks for Large-Scale Image Recognition,' arXiv:1409.1556 [cs.CV], April 2014. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, 'Going Deeper with Convolutions,' in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 1-9, June 2015. K. He, X. Zhang, S. Ren, and J. Sun, 'Deep Residual Learning for Image Recognition,' in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, pp. 770-778, June 2016. K. K. Maninis, J. Pont-Tuset, P. Arbelaez, and L. Van Gool, 'Deep Retinal Image Understanding,' in Medical Image Computing and Computer-Assisted Intervention (MICCAI), Athens, Greece, pp. 140-148, October 2016. P. F. Christ et al., 'Automatic Liver and Tumor Segmentation of CT and MRI Volumes Using Cascaded Fully Convolutional Neural Networks,' arXiv: 1702.05970 [cs.CV], February 2017. G. Chlebus, A. Schenk, J. H. Moltz, B. van Ginneken, H. K. Hahn, and H. Meine, 'Automatic Liver Tumor Segmentation in CT with Fully Convolutional Neural Networks and Object-based Postprocessing,' Scientific reports, vol. 8, no. 1, p. 15497, October 2018. X. Li, H. Chen, X. Qi, Q. Dou, C. Fu and P. Heng, 'H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes,' IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2663-2674, December 2018. Patrick Bilic et al., 'The Liver Tumor Segmentation Benchmark (LiTS),' arXiv:1901.04056v1 [cs.CV], January 2019. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64083 | - |
| dc.description.abstract | 在醫療的疾病診斷上，醫生往往需要透過不同的檢測方式得到資訊，如超音波掃描、抽血、斷層掃描等等。然而在醫療影像的判讀，只能透過肉眼判斷病變的區域，而這需要相當多的經驗累積與專業知識，且容易有誤判的情形產生。隨著影像辨識技術的進步，可透過訓練卷積式類神經網路，讓電腦自動化判讀醫療影像更加精準，藉此輔助醫生的診斷。目前影像分割的研究大多都是以全卷積網路 (FCN) 針對圖片的個別像素做分類，達到影像分割的目的。本文展示一套方法，以U-Net作為基礎架構並且匯入ResNet-50預訓練模型的參數，偵測醫療影像中的肝臟與腫瘤區域。由於資料量的限制，我們也透過調整影像光線對比度的方式、縮放圖片大小與一些基本幾何轉換達到資料擴增的效果。此外我們將訓練兩個模型，其中一個模型首先判別肝臟區域，另一個則負責判斷腫瘤區域。利用第一個模型結果作為判斷腫瘤的輸入資料，以降低其他非肝臟區域造成的雜訊。本文的訓練資料集是來自於MICCAI 2017 LiTS-challenge上的131組CT影像，並有該競賽提供的70組CT影像作為測試資料集。本篇論文所提出的方法可以達到0.71的正確率，和其他2D模型的相關研究相比，在判斷速度與正確性上皆優於其他方法。 | zh_TW |
| dc.description.abstract | For the medical diagnosis of disease, doctors often need to get information through different detection methods, such as medical ultrasound, blood test, and computed tomography. However, for the interpretation of medical images, the region of the lesion can only be distinguished by the naked eyes. This traditional method needs a lot of experiences with professional knowledge, and the possibility of misjudgment exists. With the advancement of image recognition technology, we can make computers automatically interpret medical images more precisely by training the convolutional neural network, and then help judgment by the doctors. Most of the current research on image segmentation is that using the fully convolutional network (FCN) to do pixel-wise classification for image segmentation. This paper presents a method that using U-Net as a basic architecture combining with the weights of ResNet-50 and detecting the areas of liver and tumor in the medical images. Due to the little amount of data, we implement data augmentation methods, including contrast adjustment, rescaling, geometric transformation. In addition, we train two models, one identifies the regions of the liver first, and the other is responsible for regions of the tumor. We use the results from the first model as the input data of the second model to reduce noise caused by other non-liver areas. The training data set of the paper is 131 sets of computed tomography (CT) volumes from MICCAI 2017 Liver and Liver Tumor Segmentation (LiTS) challenge, and there are 70 sets of CT volumes provided by the competition as testing dataset. Our proposed method can reach the accuracy of 0.71, and compared with other 2D models, we have better efficiency and accuracy. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:29:17Z (GMT). No. of bitstreams: 1 ntu-109-R06725047-1.pdf: 1625007 bytes, checksum: e7a43fca1e976d692519e5054535dfbc (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii LIST OF ABBREVIATIONS ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Objective 4 1.4 Thesis Organization 5 Chapter 2 Literature Review 6 2.1 Statistical Method 6 2.2 Deep Learning Method 8 2.2.1 CNN 8 2.2.2 FCN 9 2.2.3 U-Net 10 2.2.4 Transfer Learning 10 2.2.5 Related Work 11 2.2.5.a 2D Model 12 2.2.5.b 2.5D Model 12 2.2.5.c 3D Model 13 Chapter 3 Solution Approach 14 3.1 Data Pre-processing 14 3.1.1 CT Volume & Windowing 14 3.1.2 Data Augmentation 16 3.2 Training Process and Network Architecture 17 3.3 Evaluation Metrics 23 3.4 Loss Function 25 Chapter 4 Experiments 26 4.1 LiTS Dataset 26 4.2 Implementation Details 27 4.3 Results 29 Chapter 5 Conclusions and Future Work 32 5.1 Conclusions 32 5.2 Future Work 33 ACKNOWLEDGMENT 34 REFERENCES 35 | |
| dc.language.iso | en | |
| dc.subject | 肝臟 | zh_TW |
| dc.subject | 卷積式類神經網路 | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 醫療影像 | zh_TW |
| dc.subject | FCN | zh_TW |
| dc.subject | 腫瘤 | zh_TW |
| dc.subject | Medical Image | en |
| dc.subject | FCN | en |
| dc.subject | Liver tumor | en |
| dc.subject | Image Segmentation | en |
| dc.subject | U-Net | en |
| dc.subject | CNN | en |
| dc.title | 在U-Net架構上運用遷移式學習於醫療影像之肝組織及腫瘤組織區塊辨識 | zh_TW |
| dc.title | Automatic Liver and Tumor Segmentation in Medical Images Using Transfer Learning Techniques on U-Net Architecture | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃彥男,溫演福,李漢銘,莊東穎 | |
| dc.subject.keyword | 卷積式類神經網路,醫療影像,影像分割,肝臟,腫瘤,FCN, | zh_TW |
| dc.subject.keyword | CNN,Medical Image,Image Segmentation,Liver tumor,FCN,U-Net, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202000346 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-03-03 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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