北京生物醫(yī)學(xué)工程

基于深度學(xué)習(xí)的前房角開閉狀態(tài)自動(dòng)識別

Automatic recognition of the open angle and angle closure of the anterior chamber angle based on deep learning

作者：王文賽邢恩銘秦魯寧周盛楊軍林松

單位：中國醫(yī)學(xué)科學(xué)院北京協(xié)和醫(yī)學(xué)院，生物醫(yī)學(xué)工程研究所(天津 300192) 天津醫(yī)科大學(xué)眼科醫(yī)院 (天津 300384) 通信作者：林松，主管技師。E-mail：[email protected]]

關(guān)鍵詞：深度學(xué)習(xí)；前房角；超聲生物顯微鏡； VGG16；自動(dòng)識別

分類號：R318.04

出版年·卷·期（頁碼）：2021·40·3（221-226）

摘要：

目的基于深度學(xué) 習(xí) ( deep learning, DL ) 和前房角超聲生物顯微鏡 ( ultrasoundbiomicroscopy, UBM)圖像進(jìn)行前房角開閉狀態(tài)的自動(dòng)識別,為原發(fā)性閉角型青光眼的臨床自動(dòng)診斷提供輔助分析。方法數(shù)據(jù)集為天津醫(yī)科大學(xué)眼科醫(yī)院采集的眼科疾病患者的前房角 UBM 圖像,由眼科專家將 UBM 圖像分為房角開放和房角關(guān)閉兩類,按照 6∶2∶2 的比例隨機(jī)設(shè)置訓(xùn)練集、驗(yàn)證集和測試集。為提高深度學(xué)習(xí)模型的魯棒性和識別精度,對訓(xùn)練集圖像隨機(jī)進(jìn)行了旋轉(zhuǎn)、平移和反轉(zhuǎn)等不影響房角形態(tài)的數(shù)據(jù)增強(qiáng)操作。比較 VGG16、VGG19、DenseNet121、Xception 和 InceptionV3 網(wǎng)絡(luò)模型在本文數(shù)據(jù)集上的遷移學(xué)習(xí)結(jié)果,根據(jù)遷移學(xué)習(xí)結(jié)果對 VGG16 進(jìn)行卷積層和全連接層的微調(diào),用微調(diào)后的VGG16 模型實(shí)現(xiàn)前房角開閉狀態(tài)的自動(dòng)識別。用接收者操作特征曲線下面積和準(zhǔn)確率等評價(jià)指標(biāo)對模型識別結(jié)果進(jìn)行定量評價(jià),用類激活熱力圖可視化模型識別前房角開閉狀態(tài)時(shí)的主要關(guān)注區(qū)域。結(jié)果類激活熱力圖表明微調(diào)后的 VGG16 模型識別前房角開閉狀態(tài)的主要關(guān)注區(qū)域?yàn)榉拷侵行膮^(qū)域,與眼科專家的識別依據(jù)一致。該模型的識別準(zhǔn)確率為 96接收者操作特征曲線下面積為 0。結(jié)論基于深度學(xué)習(xí)和前房角 UBM 圖像能夠以較高的準(zhǔn)確率實(shí)現(xiàn)前房角開閉狀態(tài)的自動(dòng)識別,有利于原發(fā)性閉角型青光眼自動(dòng)診斷技術(shù)的發(fā)展。

Objective Based on the deep learning ( DL ) algorithm and UBM ( ultrasound biomicroscope) images of the anterior chamber angle,the automatic recognition of the open angle and angle closure of the anterior chamber angle is performed,which provides auxiliary analysis for the clinical automatic diagnosis of primary angle?closure glaucomaMethods The data set is the UBM image of the anterior chamber angle of patients with eye diseases collected by the Tianjin medical university eye hospital images into two types:open angle and closed angle,and randomly set the training set,validation set,and test set according to the ratio of 6∶2∶2 DenseNet121,Xception, and InceptionV3 network models on the data set in this article transfer learning results,fine-tune the convolutional layer and fully connected layer of VGG16,and use the fine?tuned VGG16 model to automatically realize the anterior chamber Recognition of angle opening and closing status evaluate the model recognition results,and the class activation map is used to visualize the main areas of interest when the model recognizes the opening and closing state of the anterior chamber angleResults The class activation map shows that the focus area of the fine?tuned VGG16 model is the central area of the corner 96Conclusions Based on deep learning and UBM images of the anterior chamber angle,the automatic recognition of the opening and closing state of the anterior chamber angle can be realized with high accuracy,which is beneficial to the development of automatic diagnosis technology for primary angle?closure glaucoma.

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