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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82478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 | Ting-Chen Li | en |
| dc.contributor.author | 李亭臻 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:45:30Z | - |
| dc.date.available | 2023-09-13 | |
| dc.date.copyright | 2021-11-12 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-14 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82478 | - |
| dc.description.abstract | 膠質瘤一般分成四個等級，Grade I 及 Grade II 為低惡性度，而 Grade III 及 Grade IV 為高惡性度。根據研究顯示，高惡性度的膠質瘤若是侵犯側腦室，預後結果通常較差。在已公開的方法中，目前只有測量膠質瘤有無侵犯側腦室，尚未能夠評估膠質瘤侵犯側腦室之程度。近期深度學習快速發展，在許多不同的領域都有著重大的影響。 本論文透過深度學習的方法來自動分割出側腦室區域，以及高惡性膠質瘤，得到分割完的影像後，再用影像處理方法計算出膠質瘤侵犯側腦室的距離。在深度學習方面使用的網路架構為基於 Fully Convolutional Networks (FCN) 的概念所做的改進架構 U-Net，而 U-Net 的架構有助於將向量再轉換回影像的過程中不失真。在影像處理的方法則是將深度學習完預測得到之影像，找出側腦室與腫瘤之間的最短距離，計算的過程中使用了二分搜尋法來加快運算效能。本論文使用 Dice分數做為評估分割結果的呈現，經過訓練集 258 筆資料後的模型，在測試集共 115 位病患中，分別對側腦室和腫瘤做 Dice 分數的計算，側腦室 Dice 平均為 0.91、腫瘤 Dice 平均為 0.83，而侵入程度則是從 1 毫米至 40 毫米不等。 本論文透過結合深度學習及影像處理中的距離計算之方法，來達成評估惡性膠質瘤侵犯側腦室之程度，透過此資訊可在臨床上的治療或是研究上有不同的突破。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:45:30Z (GMT). No. of bitstreams: 1 U0001-1209202123085100.pdf: 6082809 bytes, checksum: b1eb55b24290732e6c88c695f7e8bef9 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii 目錄 v 圖目錄 vii 表目錄 ix 第 一 章 緒論 1 1.1. 研究背景及動機 1 1.2. 研究目的 2 第 二 章 文獻探討 3 2.1. 醫學背景介紹 3 2.1.1. 膠質瘤 3 2.1.2. 腦部結構 4 2.2. 技術介紹 7 2.2.1. 人工智慧 7 2.2.2. 卷積神經網路 8 2.2.3. U-Net網路 12 第 三 章 材料方法 15 3.1. 材料 15 3.1.1. 來源 15 3.1.2. 收錄條件 16 3.2. 研究參數 16 3.3. 方法 17 3.3.1. 資料標註處理 18 3.3.2. 資料前處理 18 3.3.3. 側腦室與腫瘤自動分割之深度學習 19 3.3.4. 計算腫瘤與側腦室最短距離 22 第 四 章 實驗結果 26 4.1. 收錄結果 26 4.2. 資料前處理結果 26 4.3. 側腦室與腫瘤自動分割之深度學習結果 27 4.4. 計算腫瘤與側腦室最短距離結果 29 第 五 章 討論與未來研究探討 35 5.1. 討論 35 5.1.1. 資料集 35 5.1.2. 側腦室與腫瘤分割 36 5.1.3. 側腦室與腫瘤最短距離 37 5.2. 研究限制與範圍 40 5.3. 未來研究探討 41 第 六 章 結論 43 參考文獻 44 | |
| dc.language.iso | zh-TW | |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 膠質瘤 | zh_TW |
| dc.subject | Deep Learning | en |
| dc.subject | Image Processing | en |
| dc.subject | Gliomas | en |
| dc.title | 結合深度學習與影像處理評估惡性膠質瘤侵犯側腦室程度 | zh_TW |
| dc.title | Integrate Deep Learning and Image Processing to Evaluate the Extent of Ventricular Zone Invasion in High-Grade Gliomas | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳中明(Chung-Ming Li) | |
| dc.contributor.oralexamcommittee | 施博仁(Hsin-Tsai Liu),(Chih-Yang Tseng) | |
| dc.subject.keyword | 深度學習,膠質瘤,影像處理, | zh_TW |
| dc.subject.keyword | Gliomas,Deep Learning,Image Processing, | en |
| dc.relation.page | 49 | |
| dc.identifier.doi | 10.6342/NTU202103137 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-14 | |
| 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 | |
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