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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 邱奕鵬(Yih-Peng Chiou) | |
| dc.contributor.author | Chen Shuai | en |
| dc.contributor.author | 帥真 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:50:33Z | - |
| dc.date.available | 2022-07-20 | |
| dc.date.copyright | 2020-07-22 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57539 | - |
| dc.description.abstract | 老年性黃斑部病變（age-related macular degeneration, AMD）是一種常見的眼科疾病，在各個國家中都會引起中心視力的逐漸退化。它的特徵是在黃斑部出現隱節（drusen），並伴有脈絡膜新生血管（choroidal neovascularization, CNV）或地圖狀萎縮（geographic atrophy, GA）。它們的大小，數量和位置可作為疾病進展的生物標記。光學相干斷層掃描（optical coherence tomography, OCT）是獲取視網膜三維影像的一種快速且無創的方法，並且越來越多地用於監測AMD的發作和進展。AMD疾病的嚴重程度很可能由隱節和地圖狀萎縮的定量確定。由於手動分割OCT影像既費時又主觀，因此有必要開發自動圖層分割演算法。本文提出並實現了一種深度學習的OCT影像自動分割方法。利用一種以U-net架構為基礎的語義分割網路加上遞歸神經網路的架構進行分割。實驗結果表明，與其他最新方法相比，該方法大大降低了錯誤率。 | zh_TW |
| dc.description.abstract | Age-related macular degeneration (AMD) is a common eye disease that causes a gradual deterioration of central vision in various countries. It is characterized by the appearance of drusen in the macula, accompanied by choroidal neovascularization (CNV) or geographic atrophy. Their size, number, and location can serve as biomarkers for disease progression. Optical coherence tomography (OCT) is a fast and non-invasive way of obtaining three-dimensional images of the retina and is increasingly used to monitor the onset and progression of AMD. The severity of AMD disease is likely to be determined from the quantification of drusen and geographic atrophy. However, manual segmentation of OCT images is time-consuming and subjective, it is necessary to develop an automatic layer segmentation algorithm. In this paper, we propose and implement a deep learning OCT image automatic segmentation method. Using a U-net-based fully convolutional architecture and a recursive neural network for image segmentation. Experimental results show that compared with other recent methods, this method greatly reduces the error rate. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:50:33Z (GMT). No. of bitstreams: 1 U0001-2007202016270200.pdf: 10608304 bytes, checksum: 320b7bcebfad6fe4f1ab1e04962c3a39 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 緒論 1 1.1 老年性黃斑部病變 2 1.2 OCT視網膜影像分割 3 Chapter 2 基本原理 5 2.1 卷積神經網路 5 2.1.1 卷積神經網路架構概述 5 2.1.2 卷積神經網路架構 7 2.2 語義分割 20 2.3 遞歸神經網路 25 Chapter 3 方法 27 3.1 OCT數據 27 3.2 OCT影像分割 29 3.2.1 總攬 29 3.2.2 CNN影像辨識加上圖形搜索法 30 3.2.3 U-Net語義分割法 32 3.2.4 U-Net語義分割加上圖形搜索法 34 3.2.5 U-Net加上殘差LSTM語義分割法 35 Chapter 4 結果與討論 37 4.1 定量評估 40 4.2 定性評估 42 4.3 四種架構的比較與討論 52 4.4 U-Net加上殘差LSTM語義分割法 54 Chapter 5 結論 61 REFERENCES 62 | |
| dc.language.iso | zh-TW | |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | U-Net | zh_TW |
| dc.subject | 遞歸神經網路 | zh_TW |
| dc.subject | 長期短期記憶 | zh_TW |
| dc.subject | 語義分割 | zh_TW |
| dc.subject | 醫學影像處理 | zh_TW |
| dc.subject | 光學相干斷層掃描 | zh_TW |
| dc.subject | 眼科 | zh_TW |
| dc.subject | 老年性黃斑部病變 | zh_TW |
| dc.subject | convolutional neural networks | en |
| dc.subject | age-related macular degeneration | en |
| dc.subject | Ophthalmology | en |
| dc.subject | optical coherence tomography | en |
| dc.subject | medical image processing | en |
| dc.subject | semantic segmentation | en |
| dc.subject | long short-term memory | en |
| dc.subject | recurrent neural networks | en |
| dc.subject | U-Net | en |
| dc.title | 利用基於U-Net的卷積神經網路及殘差長短期記憶分割AMD患者的光學相干斷層掃描影像中的視網膜層邊界 | zh_TW |
| dc.title | Automatic Segmentation of Retinal Layer Boundaries in OCT Images of AMD Patients Using U-Net based CNN and Residual LSTM | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃升龍(Sheng-Lung Huang),吳育任(Yuh-Renn Wu) | |
| dc.subject.keyword | 卷積神經網路,U-Net,遞歸神經網路,長期短期記憶,語義分割,醫學影像處理,光學相干斷層掃描,眼科,老年性黃斑部病變, | zh_TW |
| dc.subject.keyword | convolutional neural networks,U-Net,recurrent neural networks,long short-term memory,semantic segmentation,medical image processing,optical coherence tomography,Ophthalmology,age-related macular degeneration, | en |
| dc.relation.page | 64 | |
| dc.identifier.doi | 10.6342/NTU202001655 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-21 | |
| 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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