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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁建均 | |
dc.contributor.author | Chieh-Sheng Chang | en |
dc.contributor.author | 張捷勝 | zh_TW |
dc.date.accessioned | 2021-06-17T07:00:37Z | - |
dc.date.available | 2019-08-13 | |
dc.date.copyright | 2019-08-13 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-01 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72543 | - |
dc.description.abstract | 細胞影像切割及超像素切割,皆為影像處理中很重要的課題。前者對於醫學研究及自動化診斷,帶來很大的幫助;後者則廣泛運用在電腦視覺領域中,解決各式各樣的問題。
相較於自然影像的切割,細胞影像的切割更具挑戰性。原因在於影像本身,細胞個體之間的邊界並不明顯,顏色也相似。近年來,有許多以深度學習為基礎的演算法來處理細胞影像切割的問題,像是本篇論文中所使用的V-net架構,便是一個經典的例子。本篇論文中,我們提出一些方法來改善V-net的表現。除了細胞和背景兩個類別,我們額外加入兩種邊界標記,作為不同類別來訓練。由於這些邊界的特性與細胞本體及背景有一定的差異,加入這些標記有助於提升結果。除此之外,我們也採用了基於形態學的後處理方法。藉由以上這些技巧,讓整體的準確率提升,就連影像中對比度較差的區域,也能成功切割出細胞 超像素分割最常被運用在語意切割上。好的切割方法能夠清楚定義不同物體之間的邊界,甚至更進一步能夠切開擁有相似顏色的物體。我們提出一個以最近的一個深度學習模型「超像素採樣網絡」為基礎的方法。我們加入了新的損失函數來加速訓練時的收斂速度。此外,也提出了基於傳統超像素分割演算法的後處理,來提升邊界召回率。我們用實驗顯示了,若與一些以超像素為基礎的語意切割演算法相結合,能達到較高邊界召回率的方法也能夠產生較好的語意切割結果。 | zh_TW |
dc.description.abstract | Cell image segmentation and superpixel segmentation are important topics in image processing. The former benefits for medical research and automatic diagnosis while the latter is widely applied in many other tasks of computer vision.
Cell image segmentation is more challenging than other segmentation problems since cells have similar colors and obscure boundaries. In recent years, the deep learning-based methods, including the V-net, play an important role in image segmentation. In this thesis, we propose several techniques to improve the performance of the V-net for cell segmentation. In addition to cells and background, we add two types of edge labels as different classes to train our network. Since the properties of cell edges are quite different from those of cell bodies and background, these extra labels help improve the performance. Moreover, several morphology-based post-processing algorithms are applied. With these techniques, the accuracy of cell segmentation can be much improved and the cells with poor contrast can still be well segmented. Superpixel segmentation is mostly applied in semantic segmentation. A good segmentation algorithm can precisely define the boundaries of different objects even with similar colors. We propose a method based on a recent deep learning model, Superpixel Sampling Network. We append a variance loss to the network to speed up the training session. A post-processing method based on a traditional superpixel algorithm is proposed to boost the boundary recall. We also show by experiment that methods with higher boundary recall result in better semantic segmentation results if combined with other superpixel-based segmentation algorithms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:00:37Z (GMT). No. of bitstreams: 1 ntu-108-R06942042-1.pdf: 5787435 bytes, checksum: 296000163dd5dc35fcda05b8949a45b2 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Cell Segmentation 1 1.2 Superpixel Segmentation 2 1.3 Chapter Organization 3 Chapter 2 Traditional Cell Segmentation Algorithms 4 2.1 Problem Formulation 4 2.2 Intensity Thresholding 5 2.3 Feature Extraction 5 2.4 Morphology Operations 6 2.5 Watershed Transformation (WT) 9 Chapter 3 Cell Segmentation with Deep Learning 12 3.1 The Role of Deep Learning in Cell Segmentation 12 3.2 Fully Convolutional Network (FCN) 13 3.2.1 Main structure 13 3.2.2 Convolution Layer 13 3.2.3 Max-pooling Layer 14 3.2.4 Up-sampling 15 3.3 2-D Cell Segmentation with U-net 16 3.4 3-D Cell Segmentation with V-net 17 3.4.1 Main Structure 17 3.4.2 Down Convolution Layer 18 3.4.3 Up Convolution Layer 19 3.4.4 Dice Loss 19 3.5 Convolutional Long Short-Term Memory Network 20 Chapter 4 Proposed Methods for Cell Segmentation 21 4.1 Main Structure and Contributions 21 4.2 Data Source 22 4.3 V-net 23 4.4 Post-Processing 24 4.5 Results and Conclusions 24 Chapter 5 Traditional Superpixel Algorithms 30 5.1 Problem Formulation 30 5.2 Simple Linear Iterative Clustering (SLIC) 31 5.3 Simple Non-Iterative Clustering (SNIC) 32 5.4 Entropy Rate Superpixel Segmentation (ERS) 33 5.5 Superpixels Extracted via Energy-Driven Sampling (SEEDS) 34 Chapter 6 Superpixel with Deep Learning 36 6.1 The Role of Deep Learning in Superpixel 36 6.2 Pixel Affinity Net (PAN) with Segmentation-Aware Affinity Loss (SEAL) 37 6.3 Superpixel Sampling Networks (SSN) 39 6.3.1 Main Structure 39 6.3.2 Differentiable SLIC 40 6.3.3 Loss Function 41 Chapter 7 Proposed Methods for Superpixels 43 7.1 Main Structure and Contributions 43 7.2 Variance Loss 44 7.3 Post-Processing 45 7.4 Experiment and Conclusions 46 7.4.1 Evaluation Metrics 46 7.4.2 Experiment and Results 48 7.4.3 Ablation Study of Post-Processing 53 7.4.4 Conclusions 54 Chapter 8 Conclusions and Future Work 55 REFERENCE 57 | |
dc.language.iso | en | |
dc.title | 基於深度學習的三維空間細胞切割及超像素演算法 | zh_TW |
dc.title | 3-D Cell Segmentation and Superpixel Algorithms Based on Deep Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 郭景明,許文良,劉俊麟 | |
dc.subject.keyword | 細胞影像切割,V-net,生醫影像處理,超像素切割,語意切割, | zh_TW |
dc.subject.keyword | cell image segmentation,V-net,medical image processing,superpixel segmentation,semantic segmentation, | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU201902338 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-02 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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