北京生物醫(yī)學工程

基于卷積神經(jīng)網(wǎng)絡的眼科光學相干斷層成像圖像的自動分類

Automatic classification of ophthalmic optical coherence tomographyimages based on the convolution neural network

作者：趙蒙蒙魯貞貞朱書緣王小兵馮繼宏

單位：北京工業(yè)大學環(huán)境與生命學部(北京100124) ,首都醫(yī)科大學附屬北京同仁醫(yī)院眼科中心(北京 100730) 通信作者:馮繼宏，E-mail： jhfeng@ bjut. edu. cn；王小兵，E-mail: little-bill@ 263. net

關鍵詞：眼科疾病;光學相干斷層成像;圖像處理;卷積神經(jīng)網(wǎng)絡;圖像分類

分類號：R318. 04

出版年·卷·期（頁碼）：2021·40·6（557-563）

摘要：

目的提出一種基于卷積神經(jīng)網(wǎng)絡(convolutional neural network, CNN)的眼科光學相干斷層成像(optical coherence tomography,OCT)圖像自動分類方法,實現(xiàn)對視網(wǎng)膜OCT圖像的自動分類，緩解人工診斷依賴醫(yī)生的臨床經(jīng)驗、費時費力等問題。方法基于公開的數(shù)據(jù)集2014_BOE_Srinivasan構(gòu) 建了 2個樣本數(shù)據(jù)集。其中樣本數(shù)據(jù)集一為僅對數(shù)據(jù)集中的圖像進行預處理后裁剪，樣本數(shù)據(jù)集二為對取出測試集后剩余圖像的裁剪過程中引入隨機平移和水平翻轉(zhuǎn)技術對圖像進行擴充,并劃分為訓練集和驗證集。搭建基于CNN的視網(wǎng)膜OCT圖像分類網(wǎng)絡，并分別使用兩個數(shù)據(jù)集訓練網(wǎng)絡得到分類模型。最后使用獨立的測試集對模型進行測試，并通過輸出混淆矩陣查看模型對3種類別圖像的分類情況。結(jié)果通過混淆矩陣計算得出，使用擴充后的圖像訓練的分類模型的準確度為93. 43%,靈敏度為 91.38%,特異度為95. 88%。結(jié)論提出的基于CNN的視網(wǎng)膜OCT圖像自動分類方法可以對老年性黃斑變性、糖尿病性黃斑水腫和正常3種類別的視網(wǎng)膜OCT圖像進行分類。同時,數(shù)據(jù)擴充有助于提高分類算法的性能。

Objective To propose an automatic classification method of ophthalmic optical coherence tomography ( OCT) images based on the convolutional neural network ( CNN ) , and alleviate the problems of artificial diagnosis relying on the clinical experience of ophthalmologists. Methods The two sample data sets were constructed based on the OCT data set of 2014 BOE Srinivasan. Among them, the sample data set 1 was to crop the images in the data set after preprocessing, the sample data set 2 was to augment the images by introducing the random translation and horizontal flipping technology in the images cropping of remaining images after taking out the test set, and it was divided into training set and validation set. Then a classification network based on the CNN was constructed and respectively trained by the two sample data sets to acquire the classification models. Finally, the model was tested by using the test set,and the accuracy of the model for the three types of retinal OCT images were calculated through the output confusion matrix. Results The accuracy, the sensitivity and the specificity of the model trained by augmented images were 93. 43%, 91. 38% and 95. 88%, respectively. Conclusions The proposed automatic classification method of retinal OCT images based on the CNN can classify the retinal OCT images of age-related macular degeneration, diabetic macular edema and normal. Meanwhile, data augmentation can improve the performance of classification algorithm.

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