臺灣博碩士論文加值系統

乳癌是常見的癌症，也是女性癌症死亡的主因。然而，早期的檢查和改善治療可以提升存活率。在臨床檢查上，超音波影像(US)經常用於評估乳房腫瘤的良惡性。乳房影像報告暨資料分析系統(BI-RADS)針對超音波影像的腫塊組織定義五種語彙用於評定BI-RADS等級以評估腫瘤的惡性程度。於是，我們提出一個自動BI-RADS分級系統用於腫瘤診斷。首先，我們使用基於生成對抗網路(GAN)的切割方法將每張超音波影像分成不同的影像區域以提供不同的影像資訊。然後，我們透過卷積神經網絡(CNN)預測每個語彙用於評定BI-RADS等級與評估腫瘤的良惡性。本研究使用335個經過病理驗證的腫瘤來評估我們提出的系統，其中有148個良性腫瘤和187個惡性腫瘤。在病理驗證前，所有腫瘤的最終BI-RADS分級評定為BI-RADS 3有90個，BI-RADS 4有114個，BI-RADS 5有131個。提出的系統在BI-RADS等級評定與腫瘤診斷的正確率分別為71.64% (240/335)和85.97% (288/335)。我們進一步使用CNN模型與不同的輸入影像用於評定BI-RADS等級與評估腫瘤的良惡性以比較提出的系統。使用CNN模型與原始的超音波影像在BI-RADS等級評定與腫瘤診斷的正確率分別為60.00% (201/335)和78.51% (263/335)。因此，提出的系統可以提供精確的BI-RADS等級和診斷結果給放射科醫師，而且提出的系統相較於使用CNN模型與不同的輸入影像在BI-RADS等級評定與腫瘤診斷有更良好的效能。

Breast cancer is the common and leading cause of cancer death in women worldwide. However, early examination and improved treatment can increase the survival rate. In the clinical examination, ultrasound (US) images are usually used to evaluate the malignancy of breast tumors. Breast Imaging Reporting and Data System (BI-RADS) defines five lexicons in the masses tissue of ultrasound images to assess the BI-RADS grade for evaluating tumor malignancy. Hence, we proposed an automatic BI-RADS grading system for tumor diagnosis. At first, we adopted the generative adversarial network (GAN)-based segmentation method to separate each US image into different image regions for providing different image information. Then, we predict each lexicon by the convolutional neural networks (CNN) models to assess the BI-RADS grade and evaluate tumor malignancy. There are 335 biopsy-proven tumors used to evaluate our proposed system in this study, including 148 benign tumors and 187 malignant tumors. The final BI-RADS grade assessment of all the tumors before biopsies is BI-RADS 3 for 90 cases, BI-RADS 4 for 114 cases, and BI-RADS 5 for 131 cases. The accuracy of the proposed system in the BI-RADS grade assessment and tumor diagnosis was 71.64% (240/335) and 85.97% (288/335), respectively. We further employed the CNN models directly with different input images to assess the BI-RADS grade and evaluate tumor malignancy to compare with the proposed system. The accuracy of using the CNN models with the original US images in the BI-RADS grade assessment and tumor diagnosis was 60.00% (201/335) and 78.51% (263/335), respectively. In conclusion, the proposed system can provide accurate BI-RADS grades and diagnostic results for radiologists, and the proposed system has the better performances than using the CNN models directly with different input images in the BI-RADS grade assessment and tumor diagnosis.

口試委員會審定書 i
致謝 ii
摘要 iii
Abstract iv
Table of Contents vi
List of Figures vii
List of Tables x
Chapter 1 Introduction 1
Chapter 2 Material 5
Chapter 3 Method 7
3.1 Tumor Segmentation 8
3.1.1 GAN-Based Segmentation Method 8
3.1.2 Training Details 10
3.2 Lexicon Prediction 14
3.2.1 BI-RADS Lexicons 15
3.2.2 Image Fusion Method 20
3.2.3 Classifier 22
3.3 BI-RADS Grade Assessment and Tumor Diagnosis 23
Chapter 4 Experiment Results 25
4.1 Comparison of Single Lexicon Prediction 25
4.1.1 Shape Lexicon 26
4.1.2 Orientation Lexicon 29
4.1.3 Margin Lexicon 32
4.1.4 Echo Pattern Lexicon 35
4.1.5 Posterior Features Lexicon 38
4.2 Comparison of BI-RADS Grade Assessment 43
4.3 Comparison of Tumor Diagnosis 46
4.3.1 Results of Using BI-RADS Grade 47
4.3.2 Results of Using CNN Models/NN Approach 51
Chapter 5 Discussion and Conclusions 57
References 63

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