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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張恆華 | zh_TW |
| dc.contributor.advisor | Herng-Hua Chang | en |
| dc.contributor.author | 周宇玄 | zh_TW |
| dc.contributor.author | Yu-Xuan Chou | en |
| dc.date.accessioned | 2023-08-16T16:29:04Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-16 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-09 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88947 | - |
| dc.description.abstract | 神經系統是人類體內的重要系統，負責傳遞訊息、調節身體功能與維持內部平衡等重要生命運作功能。該系統可以分為兩個部分，分別為由脊椎神經與腦神 經構成之中樞神經系統，以及中樞神經系統以外的神經構成之周邊神經系統。其中周邊神經系統之病變通常會藉由染色神經影像進行診斷，唯診斷時使用人工分 割表皮層組織範圍需要耗費大量人工成本。本研究提出一種基於深度學習自動分割神經影像中表皮層組織範圍之模型，其由不同深度的U型網路以及多尺度模塊所構成，並且提出相應之前處理演算法，輔助本研究提出模型之驗證，以有效進行訓練與測試。本研究所提出之模型架構經過訓練後的預測表皮層組織範圍成效，可以在測試資料集展現優異之分割結果，並在Dice指標顯示具有92.80%之分割準確度。 | zh_TW |
| dc.description.abstract | The nervous system is an important system for the living body, responsible for transmitting information, regulating body functions, and maintaining internal balance and other important life functions. The system can be divided into two parts: the central nervous system composed of the spinal nerves and cranial nerves and the peripheral nervous system (PNS) composed of nerves other than the central nervous system. Among them, the diseases of the PNS are usually diagnosed by stained nerve images. But the segmentation of the epidermis area in the images requires a lot of labor costs. This thesis proposes a model based on deep learning to automatically segment the epidermis area in stained nerve images. The model is composed of U-shaped networks of different depths and multi-scale modules. We also propose pre-processing algorithms to facilitate the training and testing of the proposed model. The model architecture proposed in this study to predict the epidermis area shows excellent segmentation results with the Dice index indicating segmentation accuracy of 92.80%. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T16:29:04Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-16T16:29:04Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 viii 第 1 章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 2 1.4 論文架構 3 第 2 章 文獻探討 4 2.1 類神經網路 4 2.1.1 神經元 4 2.1.2 激勵函數 5 2.2 類神經網路訓練 8 2.2.1 損失函數 8 2.2.2 優化器 10 2.2.3 反向傳播 11 2.3 卷積神經網路 11 2.4 全卷積網路 12 2.5 U-Net 14 2.6 U-Net相關架構 15 2.6.1 U-Net++ 15 2.6.2 nnU-Net 16 2.6.3 MSU-Net 16 2.6.4 MU-Net 17 第 3 章 研究設計與方法 19 3.1 方法流程 19 3.2 影像前處理 19 3.2.1 取得表皮層組織範圍中心線 20 3.2.2 測量區塊大小及切割區塊 21 3.3 卷積神經網路架構 25 3.3.1 研究網路架構 25 3.3.2 網路架構訓練 28 3.4 影像後處理 28 第 4 章 實驗結果及討論 30 4.1 染色神經切片資料集 30 4.2 網路模型訓練架構 31 4.2.1 實驗環境 31 4.2.2 訓練模型參數 32 4.3 網路訓練評估方法 33 4.4 表皮層組織區域分割結果 35 4.4.1 模型參數分析 35 4.4.2 模型預測結果 37 4.4.3 其他方法預測結果比較 42 第 5 章 結論及未來展望 48 5.1 結論 48 5.2 未來展望 48 參考文獻 50 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 基於深度學習之染色神經影像表皮層組織範圍分割 | zh_TW |
| dc.title | Epidermis Area Segmentation in Stained Nerve Images Based on Deep Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 丁肇隆;張瑞益;江明彰 | zh_TW |
| dc.contributor.oralexamcommittee | Chao-Lung Ting;Ray-I Chang;Ming-Chang Chiang | en |
| dc.subject.keyword | 深度學習,卷積神經網路,染色神經影像,影像分割, | zh_TW |
| dc.subject.keyword | deep learning,convolutional neural network (CNN),stained nerve image,image segmentation, | en |
| dc.relation.page | 53 | - |
| dc.identifier.doi | 10.6342/NTU202303041 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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