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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊曜宇(Eric Y. Chuang) | |
| dc.contributor.author | Chieh-Wen Chen | en |
| dc.contributor.author | 陳玠妏 | zh_TW |
| dc.date.accessioned | 2022-11-24T09:26:36Z | - |
| dc.date.available | 2022-11-24T09:26:36Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-28 | |
| dc.identifier.citation | [1] Health Promotion Administration, Ministry of Health and Welfare. (2021). “Overview of Colorectal Cancer Prevention and Treatment.” https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=615 pid=1126 [2] Subramanian, V., Ragunath, K. (2014). Advanced Endoscopic Imaging: A Review of Commercially Available Technologies. Clinical Gastroenterology and Hepatology, 12(3), 368–376.e1. https://doi.org/10.1016/j.cgh.2013.06.015 [3] Machida, H., Sano, Y., Hamamoto, Y., Muto, M., Kozu, T., Tajiri, H., Yoshida, S. (2004). Narrow-Band Imaging in the Diagnosis of Colorectal Mucosal Lesions: A Pilot Study. Endoscopy, 36(12), 1094–1098. https://doi.org/10.1055/s-2004-826040 [4] Vișovan, I. I., Tanțău, M., Pascu, O., Ciobanu, L., Tanțău, A. (2017). The Role of Narrow Band Imaging in Colorectal Polyp Detection. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81743 | - |
| dc.description.abstract | 大腸鏡檢查是一種有效預防大腸癌的技術，它可以發現和切除大腸息肉。切除大腸息肉可以有效的降低罹患大腸癌的風險。但是大腸鏡檢查的息肉漏檢率較高且受多種因素影響。因此，開發計算機輔助方法以支持內視鏡醫師準確分割結腸鏡影片中的息肉具有重要的意義。目前，深度學習的方法已應用於息肉分割。然而，息肉分割仍然是一個具有挑戰性的問題，因為息肉具有多種大小、顏色和紋理。並且息肉與其周圍粘膜之間的邊界並不明顯。為了解決這些問題，我們提出了一種改進的息肉分割模型，稱為 DarkraNet，用於精確分割大腸鏡影像中的息肉邊界。它是由編碼器-解碼器架構、反向注意模塊和後處理組成。此外，為了臨床實用性，我們將窄帶影像結腸鏡檢查影像添加到訓練數據中。對五個廣泛使用的息肉分割基準數據集的定量實驗結果表明，與現有方法相比，所提出的 DarkraNet 實現了最先進的分割精確度，並在準確性、通用性和臨床適用性方面進一步提升效用。此外，DarkraNet 是能夠實時分割息肉的。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T09:26:36Z (GMT). No. of bitstreams: 1 U0001-2610202112061200.pdf: 22436984 bytes, checksum: 8902676a945892125a14c525644b80fd (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 INTRODUCTION 1 1.1 Colonoscopy 1 1.2 Limitations of colonoscopy 2 1.3 Computer-aided detection system for colonoscopy 4 1.4 Deep learning-based CAD system 6 1.5 Aim 11 Chapter 2 METHODOLOGY 12 2.1 Datasets 12 2.1.1 Data acquisition 12 2.1.2 CVC-ClinicDB dataset 13 2.1.3 Kvasir-SEG dataset 13 2.1.4 NBI dataset 14 2.1.5 CVC-ColonDB dataset 14 2.1.6 ETIS-LaribPolypDB dataset 15 2.1.7 CVC-300 dataset 15 2.2 Model framework 16 2.3 Encoder 17 2.3.1 Backbone 17 2.3.2 Convolutional layer 18 2.3.3 Residual block 20 2.3.4 Pooling layer 22 2.3.5 ImageNet 23 2.3.6 Transfer learning 23 2.4 Decoder 24 2.4.1 RFB module 25 2.4.2 Dense aggregation 30 2.5 Reverse attention module 30 2.6 Post-processing 33 2.7 Implementation details 34 2.7.1 NBI colonoscopy training data 35 2.7.2 Data split 35 2.7.3 Data augmentation 35 2.7.4 Loss function 37 2.7.5 Metrics 38 2.7.6 Training settings 39 Chapter 3 RESULTS 41 3.1 Experiments on polyp segmentation 41 3.1.1 Learning Ability 41 3.1.2 Generalizability 43 3.2 Experiments on colonoscopy video 45 3.2.1 Experiments on WL colonoscopy video 46 3.2.2 Experiments on NBI colonoscopy video 47 3.3 Ablation study 49 3.3.1 Effectiveness of pretrained encoder 49 3.3.2 Effectiveness of RFB module 51 3.3.3 Effectiveness of Reverse Attention module 51 3.3.4 Effectiveness of Multi-scale training strategy 52 3.3.5 Effectiveness of loss function 54 3.3.6 Effectiveness of NBI colonoscopy training data 55 3.3.7 Effectiveness of post-processing 57 Chapter 4 DISCUSSION 58 4.1 Experiments on polyp segmentation 58 4.1.1 Learning ability 58 4.1.2 Generalizability 63 4.2 Experiments on colonoscopy video 69 4.2.1 Experiments on WL colonoscopy video 69 4.2.2 Experiments on NBI colonoscopy video 72 4.3 Limitation on polyp size measurement 74 Chapter 5 Conclusion 76 REFERENCES 77 | |
| dc.language.iso | en | |
| dc.subject | 窄帶影像 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 息肉分割 | zh_TW |
| dc.subject | 大腸鏡檢查 | zh_TW |
| dc.subject | 結直腸癌 | zh_TW |
| dc.subject | deep learning | en |
| dc.subject | narrow-band imaging | en |
| dc.subject | colorectal cancer | en |
| dc.subject | colonoscopy | en |
| dc.subject | polyp segmentation | en |
| dc.title | 使用深度學習在窄帶影像和白光大腸鏡檢查中進行實時大腸息肉分割 | zh_TW |
| dc.title | Real-time Colorectal Polyp Segmentation with Deep Learning in NBI and WL Colonoscopy | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧子彬(Hsin-Tsai Liu),賴亮全(Chih-Yang Tseng),蔡孟勳 | |
| dc.subject.keyword | 深度學習,息肉分割,大腸鏡檢查,結直腸癌,窄帶影像, | zh_TW |
| dc.subject.keyword | deep learning,polyp segmentation,colonoscopy,colorectal cancer,narrow-band imaging, | en |
| dc.relation.page | 86 | |
| dc.identifier.doi | 10.6342/NTU202104216 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-10-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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