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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83356完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松(Yeong-Sung Lin) | |
| dc.contributor.author | Chieh-Yun Cheng | en |
| dc.contributor.author | 鄭捷云 | zh_TW |
| dc.date.accessioned | 2023-03-19T21:05:29Z | - |
| dc.date.copyright | 2022-10-14 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83356 | - |
| dc.description.abstract | 在腎臟腫瘤的醫療診斷上，醫生往往需要透過電腦斷層掃描進行判斷。傳統上，醫療影像的判讀高度依賴醫生的臨床經驗與專業知識，再加上電腦斷層掃描的大量切片與低對比度導致醫生需要花費大量的時間與精力。隨著影像辨識技術的進步，開始有許多自動分割的技術產生從而輔助醫生提升診斷的效率。 本文提出一種五階段架構設計，引入了EfficientNet with noisy student作為U-Net編碼器。在第一階段分割腎臟區域，並以其為基礎在第二階段分別訓練可以針對不同腫瘤特色的模型。在第三階段綜合第二階段中三個模型的優點進行加權投票從而提升腫瘤分割的表現。接著在第四階段檢查預測結果，降低偽陰性的發生。最後，在第五階段補充腫瘤的端點區域。 我們使用2019 Kidney Tumor Segmentation Challenge (KiTS19) 資料集進行實驗，該資料集包含210位有標記的病患與90位未標記的病患。在KiTS19官網中，我們得到腎臟分割的dice分數為0.9660，腫瘤分割的dice分數為0.7407。在本研究中，我們主要有兩個貢獻。第一點為提升2D模型在腎臟與腫瘤分割的表現。第二點為加快預測速度和降低所需的計算資源，本架構可以使模型靈活應用於資料有限或運算資源有限的場景中。另外，我們以本論文所提出的架構建立了系統，希望通過這個系統讓醫生在臨床診斷更為便利。 | zh_TW |
| dc.description.abstract | In the medical diagnosis of kidneys and tumors, doctors often need to judge through computer tomography (CT). Traditionally, the interpretation of medical images is highly dependent on the clinical experience and professional knowledge of doctors. Coupled with a large number of slices and low contrast of the computed tomography scan, the doctor must spend a lot of time and energy. With the advancement of image segmentation technology, many automatic segmentation technologies have begun to be produced to assist doctors in improving the efficiency of diagnosis. This thesis proposes a five-stage architecture design and introduces EfficientNet with noisy student as U-Net encoder. The kidney region is segmented in the first stage. Based on it, models that can target different tumor characteristics are trained separately in the second stage. In the third stage, the advantages of the three models in the second stage are combined for weighted voting to improve the performance of tumor segmentation. The predicted results are then checked in the fourth stage to reduce the occurrence of false negatives. Finally, the endpoint region of the tumor is replenished in the fifth stage. We performed experiments using the 2019 Kidney Tumor Segmentation Challenge (KiTS19) dataset containing 210 labeled and 90 unlabeled patients. On the KiTS19 official website, our dice score for kidney segmentation is 0.9660, and the dice score for tumor segmentation is 0.7407. In this study, we have two contributions. The first point is to improve the performance of 2D models in kidney and tumor segmentation. The second point is to speed up the prediction and reduce the required computing resources. This architecture can make the model flexible for scenarios with limited data or computing resources. In addition, we have established a system with the architecture proposed in this thesis, hoping to make clinical diagnosis more convenient for doctors through this system. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:05:29Z (GMT). No. of bitstreams: 1 U0001-2109202212195600.pdf: 6427961 bytes, checksum: dfd9a9af4a27531e97dda49c1ea7f7bf (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員審定書i 致謝ii 摘要iii Abstract iv Contents vi List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Literature Review 6 2.1 Dataset-features-based Method . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Intensity Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Shape-based Features . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.3 Image Rotating and Cropping . . . . . . . . . . . . . . . . . . . . . 9 2.1.4 CT Image Content . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Model-based Method . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Process Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Model Improvements . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2.1 UNet Introduction . . . . . . . . . . . . . . . . . . . 12 2.2.2.2 2D UNet. . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2.3 3D UNet. . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2.4 2.5D UNet. . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2.5 Transfer Learning . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 3 Methodology 20 3.1 Training Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 First Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.2 Second Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.3 Third Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.1.4 Fourth Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.1.5 Fifth Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Data Preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.1 CT Windowing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2.2 Labeling Alternative . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.3 Data Augmentation & Filtering . . . . . . . . . . . . . . . . . . . . 33 3.3 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.1 First Stage Loss Function . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.2 Second Stage Loss Function . . . . . . . . . . . . . . . . . . . . . 37 Chapter 4 Experiments 38 4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 Training and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3.1 First Stage Training and Analysis . . . . . . . . . . . . . . . . . . . 40 4.3.2 Second Stage Training and Analysis . . . . . . . . . . . . . . . . . 42 4.3.3 Third Stage Training and Analysis . . . . . . . . . . . . . . . . . . 46 4.3.4 Fourth Stage Training and Analysis . . . . . . . . . . . . . . . . . . 48 4.3.5 Fifth Stage Training and Analysis . . . . . . . . . . . . . . . . . . 52 4.3.6 Lightweight Model Experiment . . . . . . . . . . . . . . . . . . . . 57 4.3.6.1 EfficientNetB5. . . . . . . . . . . . . . . . . . . . . 57 4.3.6.2 EfficientNetB4. . . . . . . . . . . . . . . . . . . . . 59 4.4 Compared Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 5 Conclusions and Future Work 65 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 References 68 | |
| dc.language.iso | en | |
| dc.subject | 2D U-Net | zh_TW |
| dc.subject | 腫瘤 | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 醫療影像 | zh_TW |
| dc.subject | 腎臟 | zh_TW |
| dc.subject | Image Segmentation | en |
| dc.subject | 2D U-Net | en |
| dc.subject | Tumor | en |
| dc.subject | Kidney | en |
| dc.subject | Medical Image | en |
| dc.title | 應用多階段U-Net優化模型於電腦斷層影像進行腎臟腫瘤分割 | zh_TW |
| dc.title | An Optimization-based Multi-Stage U-Net Model on Computer Tomography Images for Kidney Tumor Segmentation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李家岩(Chia-Yen Lee),孔令傑(Ling-Chieh Kung),鍾順平(Shun-Ping Chung),呂俊賢(Chun-Hsien Lu) | |
| dc.subject.keyword | 影像分割,醫療影像,腎臟,腫瘤,2D U-Net, | zh_TW |
| dc.subject.keyword | Image Segmentation,Medical Image,Kidney,Tumor,2D U-Net, | en |
| dc.relation.page | 76 | |
| dc.identifier.doi | 10.6342/NTU202203711 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-09-23 | |
| 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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