請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18720完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李明穗(Ming-Sui Lee) | |
| dc.contributor.author | Yong-Wei Chen | en |
| dc.contributor.author | 陳詠威 | zh_TW |
| dc.date.accessioned | 2021-06-08T01:21:40Z | - |
| dc.date.copyright | 2020-08-24 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-20 | |
| dc.identifier.citation | T. F. Chan and L. A. Vese, 'Active contours without edges,' in IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266-277, Feb. 2001, doi: 10.1109/83.902291. N. Zhang, J. Zhang and R. Shi, 'An Improved Chan-Vese Model for Medical Image Segmentation,' 2008 International Conference on Computer Science and Software Engineering, Hubei, 2008, pp. 864-867, doi: 10.1109/CSSE.2008.826. Y. Ebrahimdoost, J. Dehmeshki, T. S. Ellis, M. Firoozbakht, A. Youannic and S. Qanadli, 'Medical Image Segmentation Using Active Contours and a Level set Model: Application to Pulmonary Embolism (PE) Segmentation,' 2010 Fourth International Conference on Digital Society, St. Maarten, 2010, pp. 269-273, doi: 10.1109/ICDS.2010.64. J. Long, E. Shelhamer and T. Darrell, 'Fully convolutional networks for semantic segmentation,' 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3431-3440, doi: 10.1109/CVPR.2015.7298965. P. and B. T. Ronneberger Olaf and Fischer, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 2015, pp. 234–241 Y. Wang, C. Wei, Z. Wang, Q. Lu and C. Wang, 'A More Streamlined U-net for Nerve Segmentation in Ultrasound Images,' 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 101-104, doi: 10.1109/CAC.2018.8623052 L. Wu, Y. Xin, S. Li, T. Wang, P. Heng and D. Ni, 'Cascaded Fully Convolutional Networks for automatic prenatal ultrasound image segmentation,' 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 663-666, doi: 10.1109/ISBI.2017.7950607. M.Z. Alom, C. Yakopcic, M. Hasan, T.M. Taha and V.K. Asari, 'Recurrent residual u-net for medical image segmentation', Med. Imaging, vol. 6, pp. 014006, January 2019. X. Li et al., 'Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules,' 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, 2018, pp. 886-890 P. Márquez-Neila, L. Baumela and L. Alvarez, 'A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces,' in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 2-17, Jan. 2014, doi: 10.1109/TPAMI.2013.106. S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, 'Image segmentation using deep learning: A survey', arXiv:2001.05566, 2020, [online] Available: http://arxiv.org/abs/2001.05566 D. E. Cahall, G. Rasool, N. C. Bouaynaya, and H. M. Fathallah-Shaykh,“Inception modules enhance brain tumor segmentation,” Frontiers Comput. Neurosci., vol. 13, p. 44, Jul. 2019. D. Li, D. A. Dharmawan, B. P. Ng and S. Rahardja, 'Residual U-Net for Retinal Vessel Segmentation,' 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 1425-1429, doi: 10.1109/ICIP.2019.8803101. N. Ibtehaz and M. S. Rahman, “MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Networks, vol. 121, pp. 74–87, 2020, doi: https://doi.org/10.1016/j.neunet.2019.08.025. P. Felzenszwalb and D. Huttenlocher, 'Distance Transforms of Sampled Functions', 2004. M. D. Zeiler, “ADADELTA: An Adaptive Learning Rate Method,” Dec. 2012. W. Zhang, 'Deep convolutional neural networks for multi-modality isointense infant brain image segmentation', NeuroImage, vol. 108, pp. 214-224, 2015. H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, 'Pyramid Scene Parsing Network,' 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 6230-6239, doi: 10.1109/CVPR.2017.660. Z. Zhang, Q. Liu and Y. Wang, 'Road Extraction by Deep Residual U-Net,' in IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 749-753, May 2018, doi: 10.1109/LGRS.2018.2802944. O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K.Misawa, et al., 'Attention U-Net: Learning where to look for thepancreas,' arXiv:1804.03999, 2018 C. Szegedy et al., 'Going deeper with convolutions,' 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1-9, doi: 10.1109/CVPR.2015.7298594. K. He et al., “Deep residual learning for image recognition,” in Proc. CVPR, 2016, pp. 770–778 Dong H., Yang G., Liu F., Mo Y., Guo Y. (2017). Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks, in Communications in Computer and Information Science Medical Image Understanding and Analysis (Edinburgh, UK: ), 506–517. Enokiya, Y., Iwamoto, Y., Chen, Y.-W., Han, X.-H.: Automatic liver segmentation using U-Net with Wasserstein GANs. J. Image Graph. 6(2), 152–159 (2018) F. Girard, C. Kavalec, and F. Cheriet, “Joint segmentation and classification of retinal arteries/veins from fundus images,” Artificial intelligence in medicine, vol. 94, pp. 96–109, 2019 R. Almajalid, J. Shan, Y. Du and M. Zhang, 'Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation,' 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018, pp. 1103-1108, doi: 10.1109/ICMLA.2018.00179. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18720 | - |
| dc.description.abstract | 在醫學影像分割的任務中，神經網路可以分割複雜的醫學影像。但是它需要大量的訓練資料，且在pooling layer會丟失位置訊息，從而導致分割的精細度降低。在level set 方法中，並不需要訓練集，而且邊緣分割的結果更加精確，但是會因為雜訊的干擾而有極大的影響，並且需要手動標記位置。在本文中，我們提出了一個結合神經網路以及水平集方法(Level set method)，以實現自動和準確的分割方法，用於估測超音波影片內的HA體積。所提出的方法利用從深度神經網路中學習到的影像特徵來對影像做初步的強化以及找到分割的位置，接著在用水平集方法(Level set method)來將玻尿酸切割出來。我們在超音波影片資料集上測試了我們的方法，並取得良好的結果。 | zh_TW |
| dc.description.abstract | In the task of medical image segmentation, neural networks can segment complex medical images. However, it requires a lot of training data, and the location information will be lost in the pooling layer, which will reduce the fineness of segmentation results. The level set method does not require training sets, and the result of edge segmentation is more accurate. But it is greatly affected by noise, and the starting position information needs to be manually marked. In this paper, we propose a combined neural network and level set method for automated and accurate segmentation. It is used to estimate the HA volume in ultrasound video. The proposed method uses the image features learned from the deep neural network to enhance the image and find the position. Then, HA is segmented using level set method and the volume is estimated. We test our method on the ultrasound video dataset and have good results. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T01:21:40Z (GMT). No. of bitstreams: 1 U0001-1108202011200000.pdf: 2653805 bytes, checksum: 384573336c70f2170b46f40108dcba10 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 第一章 導論.................................................................................................................... 1 第二章 相關研究............................................................................................................ 3 2.1 ACWE Algorithm............................................................................................. 3 2.2 U-Net ................................................................................................................ 4 第三章 方法.................................................................................................................... 7 3.1 方法概述.......................................................................................................... 7 3.2 資料預處理...................................................................................................... 8 3.3 增強方法.......................................................................................................... 8 3.3.1 U-Net ............................................................................................................ 9 3.3.2 Enhance ........................................................................................................ 9 3.3.3 Crop .............................................................................................................11 3.4 TEMPORAL LEVEL-SET METHOD ...................................................................... 12 3.4.1 Split and resize ........................................................................................... 12 3.4.2 Generate the mask of each frame ............................................................... 14 3.4.3 Record area of mask system....................................................................... 15 3.4.4 Postprocessing ............................................................................................ 16 第四章 實驗結果與討論.............................................................................................. 17 4.1 資料集............................................................................................................ 17 4.2 實作細節........................................................................................................ 17 4.3 實驗結果........................................................................................................ 18 4.3.1 閥值比較.................................................................................................... 18 4.3.2 模型比較.................................................................................................... 19 4.3.3 間隔時間比較............................................................................................ 20 4.3.4 方法比較.................................................................................................... 25 4.3.5 TLU-Net 在喉嚨超音波影片的結果 ........................................................ 30 第五章 結論.................................................................................................................. 32 參考文獻........................................................................................................................ 33 | |
| dc.language.iso | zh-TW | |
| dc.title | 基於學習的超音波影片中玻尿酸體積的估測方法 | zh_TW |
| dc.title | A learning-based method for estimating HA volume in ultrasound videos | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曾文萱(Wen-Hsuan Tseng),葉家宏(Chia-Hung Yeh),李界羲(Jessy Lee) | |
| dc.subject.keyword | 影像分割,水平集,U-Net,超音波影片,神經網路, | zh_TW |
| dc.subject.keyword | Image segmentation,Level set,U-Net,Ultrasound video,Neural networks, | en |
| dc.relation.page | 37 | |
| dc.identifier.doi | 10.6342/NTU202002913 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1108202011200000.pdf 未授權公開取用 | 2.59 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。