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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 王偉仲(Weichung Wang) | |
| dc.contributor.author | Chengen Lee | en |
| dc.contributor.author | 李承恩 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:49:17Z | - |
| dc.date.available | 2030-01-01 | - |
| dc.date.copyright | 2020-08-06 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-04 | |
| dc.identifier.citation | Y.Boykov,V.Kolmogorov,D.Cremers,and A.Delong.An integral solution to surface evolution pdes via geo-cuts. InECCV (3), pages 409–422,2006. E. Grøvik, D. Yi, M. Iv, E. Tong, D. Rubin, and G. Zaharchuk. Deep learning enables automatic detection and segmentation of brain metastases on multi sequence mri. Journal of Magnetic Resonance Imaging, 51(1):175–182, May 2019. S.-Y. Hu, W.-H. Weng, S.-L. Lu, Y.-H. Cheng, F. Xiao, F.-M. Hsu, and J.-T. Lu. Multimodal volume-aware detection and segmentation for brain metastases radiosurgery, 2019. F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal,G. Koehler, T. Norajitra, S. Wirkert, and K. H. Maier-Hein. nnu-net: Self-adapting framework for u-net-based medical image segmentation, 2018. F. Isensee, M. Schell, I. Pflueger, G. Brugnara, D. Bonekamp, U. Neuberger,A. Wick, H. Schlemmer, S. Heiland, W. Wick, and et al. Automated brain extraction of multi sequence mri using artificial neural networks.Human Brain Mapping,40(17):4952–4964, Aug 2019. H. Kervadec, J. Bouchtiba, C. Desrosiers, Éric Granger, J. Dolz, and I. B. Ayed. Boundary loss for highly unbalanced segmentation, 2018. E. J. Kirkland.Bilinear Interpolation, pages 261–263. Springer US, Boston, MA, 2010. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection, 2017. C. Liu, L.-C. Chen, F. Schroff, H. Adam, W. Hua, A. Yuille, and L. Fei-Fei. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,2019. F. Milletari, N. Navab, and S.-A. Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation, 2016 A. Myronenko. 3d mri brain tumor segmentation using autoencoder regularization, 2018. O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori,S. McDonagh, N. Y. Hammerla, B. Kainz, B. Glocker, and D. Rueckert. Attention u-net: Learning where to look for the pancreas, 2018. R. Olivier and C. Hanqiang. Nearest neighbor value interpolation. InternationalJournal of Advanced Computer Science and Applications, 3(4), 2012. O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation, 2015. A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken,A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M.Summers, P. Bilic, P. F. Christ, R. K. G. Do, M. Gollub, J. Golia-Pernicka, S. H.Heckers, W. R. Jarnagin, M. K. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein,and M. J. Cardoso. A large annotated medical image dataset for the development and evaluation of segmentation algorithms, 2019. N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich,and J. C. Gee. N4itk: improved n3 bias correction.IEEE transactions on medical imaging, 29(6):1310–1320, 06 2010. P. Wang and A. C. S. Chung. Focal dice loss and image dilation for brain tumor segmentation. In D. Stoyanov, Z. Taylor, G. Carneiro, T. Syeda-Mahmood, A. Martel,L. Maier-Hein, J. M. R. Tavares, A. Bradley, J. P. Papa, V. Belagiannis, J. C. Nasci-mento, Z. Lu, S. Conjeti, M. Moradi, H. Greenspan, and A. Madabhushi, editors,Deep Learning in Medical Image Analysis and Multimodal Learning for ClinicalDecision Support, pages 119–127, Cham, 2018. Springer International Publishing. Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A. L. Yuille, and D. Xu. C2fnas: Coarse-to-fine neural architecture search for 3d medical image segmentation, 2019. Z. Zhu, C. Liu, D. Yang, A. Yuille, and D. Xu. V-nas: Neural architecture search for volumetric medical image segmentation, 2019. B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning, 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15643 | - |
| dc.description.abstract | 從圖像註釋標籤對於醫生在醫學圖像問題的工作流程中是很費時的。因此，我們的目標是建立一個自動分割流程，以幫助醫生進行註釋。整體工作流程分為預處理，模型訓練，後處理和驗證。對於模型的結構，我們使用由NVIDIA使用神經體系結構搜索（NAS）構建的粗到細神經結構搜索（C2FNAS）模型。在本文中，我們將焦點骰子損失與邊界損失相結合並比較性能，然後通過實驗觀察到，與僅使用焦點骰子損失的情況相比，邊界損失使訓練收斂更快，並且獲得了更好的性能。最後，我們從臺灣大學附屬醫院（NTUH）的數據集中獲得了最好的平均骰子得分為0.81。 | zh_TW |
| dc.description.abstract | Annotating the label from the images is time-consuming for doctors in the workflow of the medical image problem. Thus, we aim to build an automatic segmentation workflow to help doctor for annotation. The workflow is divided into pre-processing, model training, post-processing, and validation.For the architecture of model, we use the Coarse-to-Fine Neural ArchitectureSearch (C2FNAS) model builded by NVIDIA using the Neural ArchitectureSearch (NAS). In this thesis, we combine focal dice loss with boundary loss to compare performance, and then we observe by experiments that boundary loss make the convergence of training faster and get the better performance than the condition which only uses focal dice loss. Finally, we get the mean dice score 0.81 in our dataset which is from the National Taiwan UniversityHospital (NTUH). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:49:17Z (GMT). No. of bitstreams: 1 U0001-0308202017040000.pdf: 6939350 bytes, checksum: 86445b5fe45d0798bdaff811760adff5 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 iii 誌謝 v Acknowledgements vii 摘要 ix Abstract xi 1 introduction 1 1.1 Background 1 1.2 Problem Description 2 2. Related Work 5 2.1 Medical Image Segmentation 5 2.2 Brain Metastases Segmentation 6 2.3 Neural Architecture Search (NAS) 6 3 Methodology 9 3.1 Dataset 9 3.2 Pre-processing 10 3.2.1 Resampling 10 3.2.2 Normalization 11 3.2.3 N4 Bias Field Correction 12 3.2.4 Skull Moving 13 3.2.5 Tumor Clustering 13 3.3 C2FNAS 14 3.3.1 Coarse Stage 14 3.3.2 Fine Stage 15 3.4 Loss 17 3.4.1 Focal Dice Loss 17 3.4.2 Boundary Loss 18 4 Experiment 23 4.1 Parameters and Model Setting 23 4.2 Training 24 4.3 Result 25 4.3.1 Patient Based 25 4.3.2 Tumor Based 26 4.4 External Result 28 5 Discussion 31 5.1 Future Work 34 6 Conclusion 37 Bibliography 39 | |
| dc.language.iso | en | |
| dc.title | 使用具有邊界損失的從粗到細神經結構搜索進行三維腦轉移瘤分割 | zh_TW |
| dc.title | Three Dimensional Brain Metastases Segmentation Using Coarse-to-Fine Neural Architecture Search with Boundary Loss | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖偉智(Weichih Liao),李宏毅(Hungyi Lee) | |
| dc.subject.keyword | 醫學影像分析,深度學習,卷積神經網路, | zh_TW |
| dc.subject.keyword | Medical Image Analysis,Deep Learning,Convolution Neural Network, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU202002296 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-04 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 應用數學科學研究所 | zh_TW |
| dc.date.embargo-terms | 2030-01-01 | |
| Appears in Collections: | 應用數學科學研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| U0001-0308202017040000.pdf Restricted Access | 6.78 MB | Adobe PDF |
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