基於卷積神經網絡之乳房彈性超音波影像電腦輔助診斷

Computer-Aided Tumor Diagnosis for Breast Elastography Based on Convolutional Neural Network

沈予涵(Yu-Hang Shen)

指導教授 ： 張瑞峰

國立臺灣大學/電機資訊學院/生醫電子與資訊學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201703829

摘要

近年來，乳癌一直是女性癌症的主要死因之一。早期發現與早期治療是提高乳癌存活率最重要的手段之一。許多研究顯示，乳房彈性超音波（Breast Elastography）的乳癌檢出率，較傳統 B-mode 超音波檢查的診斷檢出率要高出許多。傳統電腦輔助診斷的方法，通常需要先做腫瘤影像切割，並且使用人工定義的特徵做腫瘤分類。近年來，隨著電腦硬體技術的革新，卷積神經網絡（Convolutional Neural Network, CNN）正積極的被應用在各個領域中，其中最重要的應用即為影像辨識。使用卷積神經網絡做醫學影像辨識的研究也陸續被提出，卷積神經網絡與傳統方法相比，優勢在於不須先做影像切割，及特徵是經由神經網絡自動學習擷取，可以更廣泛的應用在各種影像上，並且有效避免傳統方法對資料過擬合（Data Overfitting）的問題。本研究主要的目的是利用卷積神經網絡進行乳房彈性超音波影像的電腦輔助診斷。首先，在 B-mode 影像上選定腫瘤區域，並在彈性影像上擷取與之對應的腫瘤區域，將這兩個腫瘤區域輸入由本研究提出的卷積神經網絡架構中進行特徵學習和擷取，最後以學習到的特徵診斷腫瘤的良惡性。本研究的實驗以151個經過病理驗證的病例進行測試，包含89個良性以及62個惡性的病例。經由實驗結果，當結合B-mode與彈性影像訓練卷積神經網絡時，診斷的準確率為85.43% (129/151)，靈敏性80.65% (50/62)，特異性 88.76% (79/89)，以及ROC曲線面積0.9202。因此可知，乳房彈性超音波結合卷積神經網絡可以有效的診斷腫瘤良惡性。

關鍵字

乳癌 ； 乳房彈性超音波 ； 電腦輔助診斷 ； 卷積神經網絡

並列摘要

Breast cancer has become one of the most frequently diagnosed cancers and one of the major causes of cancer death in women. Early detection and treatment are the most effective way to reduce the mortality rate. Recently, many studies have shown that compared with using only conventional B-mode ultrasound, the examination with elastography can improve the diagnostic performance. The conventional computer-aided diagnosis (CADx) methods usually require tumor segmentation, and use hand-crafted features to classify the tumors. For the past few years, due to the innovation of graphics processing unit (GPU) technique, the convolutional neural network (CNN) continues to thrive. Nowadays, CNN has been applied to various fields, especially in computer vision, and has rapidly become a methodology of choice for analyzing medical images. Compared to conventional CADx methods, CNN does not need to segment the tumor manually and can learn the features automatically. CNN can also apply to various image types, and can prevent the overfitting problem in conventional methods. In this study, images of 151 biopsy-proved sonoelastography cases composed of 89 benign and 62 malignant cases are used to evaluate the diagnosis performance. According to the experimental results, the performance of utilizing both B-mode images and elastography images to train the proposed architecture achieved an accuracy of 85.43% (129/151), a sensitivity of 80.65% (50/62), a specificity of 88.76% (79/89), and an AUC value of 0.9202. This thus verifies the feasibility of utilizing breast elastography together with CNN for effective classification of breast tumors.

並列關鍵字

Breast Cancer ； Elastography ； Computer-Aided Diagnosis (CADx) ； Convolutional Neural Network (CNN)

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