臺灣博碩士論文加值系統

在物件偵測上，物體位置的標示經常以方框表示；然而在醫療影像中，有許多物件都有特定的幾何形狀，如圓形的瞳孔、橢圓的血球。在本研究中我們提出一種結構化區域提取網路的方法，以橢圓的錨點形狀偵測瞳孔、血球的輪廓，相比於方框的表示形式，物件輪廓的偵測方式會更接近於實體物件模樣。我們所提出的錨點形狀計算方法，能夠應用在許多現有的物件偵測網路架構中，實驗中，我們分別應用於兩個不同的偵測網路：Mask R-CNN與SSD，在其網路架構中，加入結構化的區域提取網路，分別對應瞳孔與血球偵測，用以直接估計出影像中的瞳孔、血球幾何定位參數。
實驗中，使用 Mask R-CNN 進行瞳孔定位的偵測結果，其 Dice Similarity Coefficient (DSC) 相似性係數達0.93，且在1230個瞳孔影像中，可正確偵測到1227個。而在使用 SSD 作血球偵測、計數實驗中，我們使用了公開的血球資料庫 ALL-IDB，其所有影像的血球個數約有 39000 顆，我們將資料分60%用於訓練、20%用於驗證，20%用於測試，偵測結果在 F1-Score 與 DSC 數據分別達到 97.45% 及 91.62%，並在部份重疊血球偵測上顯示出較方框精確的偵測結果，然而，在血球的旋轉角度定位上，平均誤差值約在25度左右，還有改善的空間。

In object detection, bounding boxes are often used as basic representations to localize objects. However, in medical images, many target objects have specific geometric shapes, such as circular pupils and elliptical blood cells. In this study, we propose a new structural region proposal network (RPN) that adopts parametric, elliptical anchor shapes to detect pupils and blood cells in medical images. Comparing with the adoption of bounding boxes as anchors in conventional RPN, the proposed elliptical anchor shapes and structural RPN may fit actual object shapes better. The proposed structural RPN can also be seamlessly integrated to many existing detection networks. In our experiments, the structural RPN is integrated into Mask R-CNN for pupil localization and SSD for blood cell detection to localize target objects and to estimate their shape parameters.
In the experiments for pupil location, the Dice similarity coefficient (DSC) reached 0.93 by using the Mask R-CNN with structural RPN. Among 1230 test pupil images, 1227 pupils are correctly detected. For blood cell detection and counting using SSD with structural RPN, a public blood cell database, ALL-IDB, which contains about 39,000 blood cells in images, is used. We use 60%, 20% and 20% of the data samples for training, validation and testing, respectively. The testing results of F1-score and DSC are 97.45% and 91.62%, respectively. The proposed approach using anchor shapes also has better qualitative performance in detecting occluded blood cells than the original SSD using bounding boxes. However, in estimating rotation angles of blood cells, the average estimation error of rotation is about 25 degrees, which leaves room for future improvement.

摘要 I
ABSTRACT II
致謝 IV
CONTENTS V
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1 Introduction 1
1.1 Application for Pupil Localization 2
1.2 Application for Blood Cells Detection and Counting 3
1.3 Related Work 4
1.3.1 Pupil Detection 4
1.3.2 Blood Cell Detection 5
1.3.3 Deep Learning 5
1.4 Overview of The Proposed Methods and Thesis Organization 7
CHAPTER 2 Structural RPN 9
2.1 Method 9
2.2 Anchor Ellipse Extension 10
2.3 Elliptic Ambiguity Processing 12
2.4 Additional Features in Extending One-Stage SSD 13
2.4.1 Anchor Ellipse Matching in Training 14
2.4.2 Non-maximum Suppression in Testing 15
CHAPTER 3 Structural RPN for Mask R-CNN and SSD 18
3.1 Mask R-CNN 18
3.1.1 ROI-Align 20
3.2 SSD with Feature Pyramid Network (FPN/SSD) 22
CHAPTER 4 Experimental Results 24
4.1 Feasibility Verification for Mask R-CNN with Elliptical Anchors 24
4.2 Feasibility Verification for SSD with Elliptical Anchors 25
4.3 Pupil Localization Results 27
4.4 Pupil Localization Results Using Structural RPN with Circular Anchors 33
4.5 Blood Cells Counting Results 36
4.5.1 Evaluation Statistics 39
4.5.2 Evaluations Result 39
CHAPTER 5 Conclusion 45
5.1 Discussions and Future Work 45
REFERENCES 47

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