請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82479完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 梁祥光(Hsiang-Kuang Liang) | |
| dc.contributor.author | An Liu | en |
| dc.contributor.author | 劉安 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:45:31Z | - |
| dc.date.available | 2023-09-13 | |
| dc.date.copyright | 2021-11-12 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-13 | |
| dc.identifier.citation | 1. Ostrom, Q.T., et al., The epidemiology of glioma in adults: a “state of the science” review. Neuro-oncology, 2014. 16(7): p. 896-913. 2. Louis, D., Ohgaki h, Wiestler OD, Cavenee WK, burger PC, Jouvet A, scheithauer bW and Kleihues P: The 2007 WhO classification of tumours of the central nervous system. Acta Neuropathol, 2007. 114(2): p. 97-109. 3. Liang, H.-K., et al., Preoperative prognostic neurologic index for glioblastoma patients receiving tumor resection. Annals of surgical oncology, 2014. 21(12): p. 3992-3998. 4. Wen, P.Y. and S. Kesari, Malignant gliomas in adults. New England Journal of Medicine, 2008. 359(5): p. 492-507. 5. El-Zein, R., M. Bondy, and M. Wrensch, Epidemiology of brain tumors, in Brain tumors. 2005, Springer. p. 3-18. 6. Lacroix, M., et al., A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. Journal of neurosurgery, 2001. 95(2): p. 190-198. 7. Grover, V.P., et al., Magnetic resonance imaging: principles and techniques: lessons for clinicians. Journal of clinical and experimental hepatology, 2015. 5(3): p. 246-255. 8. Schag, C.C., R.L. Heinrich, and P.A. Ganz, Karnofsky performance status revisited: reliability, validity, and guidelines. Journal of Clinical Oncology, 1984. 2(3): p. 187-193. 9. Cohen, A.L., S.L. Holmen, and H. Colman, IDH1 and IDH2 mutations in gliomas. Current neurology and neuroscience reports, 2013. 13(5): p. 345. 10. Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25: p. 1097-1105. 11. Deng, J., et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE conference on computer vision and pattern recognition. 2009. Ieee. 12. Simonyan, K. and A. Zisserman, Very deep convolutional networks for large- scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 13. He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. 14. Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEEconference on computer vision and pattern recognition. 2015. 15. Lei, T., et al., Medical image segmentation using deep learning: a survey. arXiv preprint arXiv:2009.13120, 2020. 16. Li, Q., et al. Medical image classification with convolutional neural network. in 2014 13th international conference on control automation robotics vision (ICARCV). 2014. IEEE. 17. Pereira, S., et al., Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging, 2016. 35(5): p. 1240-1251. 18. Díaz-Pernas, F.J., et al. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. in Healthcare. 2021. Multidisciplinary Digital Publishing Institute. 19. Islam, M., et al. Brain tumor segmentation and survival prediction using 3D attention UNet. in International MICCAI Brainlesion Workshop. 2019. Springer. 20. Sun, L., et al., Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Frontiers in neuroscience, 2019. 13: p. 810. 21. Baid, U., et al., Overall survival prediction in glioblastoma with radiomic features using machine learning. Frontiers in computational neuroscience, 2020. 14: p. 61. 22. Wang, S., et al. Automatic brain tumour segmentation and biophysics-guided survival prediction. in International MICCAI Brainlesion Workshop. 2019. Springer. 23. Pei, L., et al., Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images. Scientific Reports, 2020. 10(1): p. 1-11. 24. Suter, Y., et al. Deep learning versus classical regression for brain tumor patient survival prediction. in International MICCAI Brainlesion Workshop. 2018. Springer. 25. Ioffe, S. and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. in International conference on machine learning. 2015. PMLR. 26. Lin, M., Q. Chen, and S. Yan, Network in network. arXiv preprint arXiv:1312.4400, 2013. 27. Schlemper, J., et al., Attention gated networks: Learning to leverage salientregions in medical images. Medical image analysis, 2019. 53: p. 197-207. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82479 | - |
| dc.description.abstract | "膠質瘤 (Glioma) 為成人常見的腦瘤，約81%的惡性腦瘤皆為膠質瘤，根據世界衛生組織 (World Health Organization ,WHO) 的病理分級系統，膠質瘤可依據其病理特徵區分成四級，第三級和第四級合起來稱為惡性膠質瘤。其中第四級的多形性膠質母細胞瘤 (Glioblastoma ,GBM) 是最常見且非常具有侵略性的腦瘤，惡性膠質瘤中約有60-70%屬於GBM，而第三級的anaplastic astrocytoma約佔了10-15%，GBM病人存活中位數約12-15個月，低於5%的患者能生存超過5年。一般來說醫師在判斷罹患惡性膠質瘤患者的存活預後時，會根據病患未開刀的核磁共振影像、年齡、Karnofsky Performance Score、組織切片 (例如：分子、生物標記) 和腫瘤位置、大小⋯⋯等等，做綜合的判定。同樣是罹患惡性膠質瘤的患者，存活時間有長有短，短的數月，長的可超過兩年。我們想要探討是否能使用病人未接受治療的腦部MRI影像，來輔助判斷其存活預後。因此我們使用Brain tumor segmentation challenge這個競賽2020年所提供的資料MRI影像中T1+C (注射顯影劑後的T1序列，可觀察腫瘤以及壞死區域) 和Fluid Attenuated Inversion Recovery序列 (可觀察腫瘤周邊水腫)，結合深度學習中VGG-16的模型架構做延伸進行訓練，找出影像中關鍵的特徵，希望預測患者存活期能否超過或未超過13.1個月(400天)。經過5-fold 的cross-validation驗證，最終我們達到Accuracy為57.6 ± 1.8%，Sensitivity為60.2 ± 1.4%，Specificity為54.65 ± 5.2%，最後AUC為0.551。結果顯示單用病人未接受治療的腦部MRI影像 (T1+C、FLAIR) 準確度有限，需加上其他因子輔助判斷其存活預後。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:45:31Z (GMT). No. of bitstreams: 1 U0001-1209202122094300.pdf: 4846928 bytes, checksum: 8a556d4387a2aad6d51c5e79b4c7775e (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝............................................................................................................... I 中文摘要...................................................................................................... II Abstract .......................................................................................................III 目錄..............................................................................................................V 圖目錄......................................................................................................VIII 表目錄..........................................................................................................X 第一章 緒論..............................................................................................1 1.1. 研究背景與動機...............................................................................1 1.2. 研究目的...........................................................................................3 第二章 文獻回顧......................................................................................4 第三章 研究材料與方法..........................................................................7 3.1. 資料來源...........................................................................................7 3.2. 資料前處理.......................................................................................9 3.2.1. 影像正規化 (Normalization).....................................................9 3.2.2. 切割影像 Volume of interest (VOI)..........................................10 3.2.3. 影像增量 (Augmentation) .......................................................11 3.3. 研究方法.........................................................................................12 3.3.1. VGG-16......................................................................................13 3.3.2. Attention Gated ..........................................................................17 3.4. Performance metrics .........................................................................19 第四章 研究結果....................................................................................22 4.1. 資料收集結果.................................................................................22 4.2. 資料前處理結果.............................................................................23 4.2.1. 影像正規化結果.......................................................................23 4.2.2. 切割 VOI 結果..........................................................................27 4.2.3. 影像增量結果...........................................................................28 4.3. 存活預後分類結果.........................................................................30 4.3.1. VGG-16 延伸架構訓練結果.....................................................30 4.3.1. 加入 Attention Gated 結果 .......................................................31 第五章 討論............................................................................................33 5.1. 比較不同 filter 數量之分類結果 ...................................................35 5.2. 比較加入患者年紀與未加入患者年紀之分類結果.....................38 5.3. 僅使用患者年紀之分類結果.........................................................40 5.4. 未來展望.........................................................................................41 第六章 結論............................................................................................43 文獻參考.....................................................................................................44 | |
| dc.language.iso | zh-TW | |
| dc.subject | 核磁共振影像 | zh_TW |
| dc.subject | 惡性膠質瘤 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 存活期分類 | zh_TW |
| dc.subject | malignant gliomas | en |
| dc.subject | Deep learning | en |
| dc.subject | overall survival classification | en |
| dc.subject | Magnetic Resonance Imaging | en |
| dc.title | 評估結合深度學習與腦部核磁共振影像預測惡性膠質瘤病人存活預後之可行性 | zh_TW |
| dc.title | Evaluating the Feasibility of Combining Deep Learning and Brain MRI Images to Predict Survival of Patients with Malignant Glioma | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳中明(Chung-Ming Chen) | |
| dc.contributor.oralexamcommittee | 施博仁(Hsin-Tsai Liu),(Chih-Yang Tseng) | |
| dc.subject.keyword | 惡性膠質瘤,深度學習,存活期分類,核磁共振影像, | zh_TW |
| dc.subject.keyword | malignant gliomas,Deep learning,overall survival classification,Magnetic Resonance Imaging, | en |
| dc.relation.page | 46 | |
| dc.identifier.doi | 10.6342/NTU202103135 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-09-13 | - |
| 顯示於系所單位： | 醫學工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1209202122094300.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 4.73 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。