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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃升龍(Sheng-Lung Huang) | |
| dc.contributor.author | Shu-Wen Chang | en |
| dc.contributor.author | 張淑雯 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:17:36Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:17:36Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80815 | - |
| dc.description.abstract | 在皮膚相關的研究中，提到患有疾病的皮膚與正常皮膚的細胞核型態存在差異，若能及時提供臨床醫師皮膚細胞核的量化資訊，有機會提早診斷出皮膚是否異常。在各種皮膚疾病種類裡，基底細胞癌(Basal cell carcinoma；BCC)為常見的皮膚癌，病理學家會以組織切片的染色影像作為黃金標準來診斷，然而組織染色切片的製備流程較為耗時且複雜，若能直接從組織切片辨識出BCC，有機會加快診斷速度，減少病患忍受病痛的時間。 光學同調斷層掃描(Optical coherence tomography；OCT) 能以非侵入的方式呈現具細胞等級解析度的活體皮膚縱切面影像，也能在不將組織切片染色的情況下，提供高解析度的檢體組織影像，近年來，卷積神經網絡 (Convolutional neural network；CNN)被廣泛應用於圖像分類、目標檢測以及影像分割，其具有自動抓取影像特徵的優勢。本篇論文結合OCT成像技術與深度學習演算法，利用CNN分別從活體正常皮膚OCT縱切面影像以及檢體基底細胞癌OCT影像標記出細胞核以及基底細胞癌輪廓。針對活體正常皮膚OCT縱切面影像，細胞核分割模型mIoU達72.4%±7.8%，透過細胞核分割模型獲取細胞核標記影像，並對細胞核做量化分析，探討正常皮膚表皮層的細胞核特徵。平均細胞核大小的計算結果為20.11±4.77 μm2 (範圍：11.39 ~33.49 μm2)，另外也計算不同深度下細胞核的長短軸比，呈現正常皮膚表皮層細胞核上扁下圓的分佈趨勢。針對檢體基底細胞癌OCT影像，透過改變損失函數以及加入分類模型提升分割模型的特異性，最終patch-based準確率達87.8%±6.8%，mIoU達60.3%±10.1%。希望透過深度學習演算法的輔助，有望促進皮膚疾病的診斷與治療。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:17:36Z (GMT). No. of bitstreams: 1 U0001-0410202114003500.pdf: 6273350 bytes, checksum: 9d2a427934dbe7078db7830d6c18c029 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 I 摘要 II Abstract III 圖目錄 VII 表目錄 XI 第一章 緒論 1 第二章 全域式光學同調斷層掃描術與皮膚結構與樣本製備介紹 3 2.1光學同調斷層掃描 3 2.1.1時域式光學同調斷層掃描 4 2.1.2全域式時域光學同調斷層掃描 6 2.1.3 Mirau-based全域式時域光學同調掃描 7 2.1.4 Ce3+:YAG 與Ti:sapphire Mirau-based OCT系統 9 2.2人類皮膚構造與組織病理切片製備 11 2.2.1皮膚結構與功能以及基底細胞癌 11 2.2.2組織病理切片製備流程 15 第三章 卷積神經網路在OCT影像之應用 20 3.1卷積神經網路在影像分割之應用 20 3.1.1卷積神經網路的基本原理 21 3.1.2卷積神經網路應用於影像分割 28 3.1.3過擬合減緩方式 33 3.1.4遷移學習 35 3.2皮膚OCT影像資訊及預處理方法 36 3.2.1人類活體皮膚影像資訊與預處理 36 3.2.2基底細胞癌檢體皮膚影像資訊與預處理 41 3.2.3影像分割資料集建立 43 第四章 影像分割模型之結果與分析 46 4.1 人類皮膚細胞核分割結果及分析 47 4.1.1使用遷移學習的差異 48 4.1.2類別不平衡處理 51 4.1.3影像分割後處理(Post processing) 53 4.2人類皮膚細胞核量化分析 55 4.3基底細胞癌分割結果及分析 58 4.3.1使用不同損失函數 59 4.3.2影像後處理 63 4.3.3使用分類模型改善分割結果 65 第五張 結論與未來展望 69 5.1結論 69 5.2未來展望 70 參考文獻 71 附錄1 分割資料集的標記工具 76 附錄2 T-Blue影像基底細胞癌分割結果 81 | |
| dc.language.iso | zh-TW | |
| dc.subject | 基底細胞癌 | zh_TW |
| dc.subject | 光學同調斷層掃描(OCT) | zh_TW |
| dc.subject | 卷積神經網路(CNN) | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 活體皮膚 | zh_TW |
| dc.subject | 檢體皮膚 | zh_TW |
| dc.subject | 細胞核 | zh_TW |
| dc.subject | In vivo skin | en |
| dc.subject | Basal cell carcinoma | en |
| dc.subject | Nucleus | en |
| dc.subject | Ex vivo skin | en |
| dc.subject | Optical coherence tomography | en |
| dc.subject | Convolutional neural network | en |
| dc.subject | Segmentation | en |
| dc.title | 利用深度學習演算法分析具細胞解析度的人類皮膚光學同調斷層掃描影像 | zh_TW |
| dc.title | Analysis of the Human Skin Tomographic Cellular-Resolution Images Utilizing the Deep Learning Algorithm | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳育弘(Hsin-Tsai Liu),陳宏銘(Chih-Yang Tseng),曾雪峰 | |
| dc.subject.keyword | 光學同調斷層掃描(OCT),卷積神經網路(CNN),影像分割,活體皮膚,檢體皮膚,細胞核,基底細胞癌, | zh_TW |
| dc.subject.keyword | Optical coherence tomography,Convolutional neural network,Segmentation,In vivo skin,Ex vivo skin,Nucleus,Basal cell carcinoma, | en |
| dc.relation.page | 82 | |
| dc.identifier.doi | 10.6342/NTU202103530 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-06 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 光電工程學研究所 | zh_TW |
| 顯示於系所單位： | 光電工程學研究所 | |
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