北京生物醫(yī)學(xué)工程

基于非結(jié)節(jié)自動分類的二維卷積網(wǎng)絡(luò)在肺結(jié)節(jié)檢測假陽性減少中的應(yīng)用

The two-dimensional convolution network based on non nodule automatic classification for reduction of false positivity in pulmonary nodule detection

作者：任敬謀李曉琴

單位：北京工業(yè)大學(xué)環(huán)境與生命學(xué)部(北京 100124)

關(guān)鍵詞：醫(yī)學(xué)影像處理; 計算機(jī)斷層掃描; 肺結(jié)節(jié)檢測; 卷積神經(jīng)網(wǎng)絡(luò); 深度學(xué)習(xí)

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·4（389-397）

摘要：

目的針對計算機(jī)斷層掃描(computed tomography, CT)圖像的肺結(jié)節(jié)自動檢測中靈敏度低及存在大量假陽性的問題,本文提出了一種基于非結(jié)節(jié)自動分類的二維卷積神經(jīng)網(wǎng)絡(luò)( convolutional neural network, CNN),并用于肺結(jié)節(jié)檢測中的假陽性減少。方法首先對 CT 圖像進(jìn)行預(yù)處理,通過對原始 CT 圖像重采樣和歸一化,解決不同樣本像素間隔不一致及圖像對比度不統(tǒng)一問題;采用結(jié)節(jié)不同空間方向的二維切片信息采集進(jìn)行正樣本擴(kuò)充,負(fù)樣本無監(jiān)督分類方法平衡正負(fù)樣本數(shù)量;分別利用不同類別負(fù)樣本與正樣本訓(xùn)練二維卷積神經(jīng)網(wǎng)絡(luò),獲得多個用于降低假陽性的 2D CNN 肺結(jié)節(jié)檢測模型,對LUNA16 提供的假陽性減少數(shù)據(jù)集進(jìn)行五折交叉驗證,利用官方提供的評估程序?qū)δＰ瓦M(jìn)行評估。結(jié)果通過與直接使用單個 2D CNN 進(jìn)行分類的模型比較,對非結(jié)節(jié)分類后訓(xùn)練多個模型的分類結(jié)果較佳,最終競爭性指標(biāo)(competition performance metric,CPM)競爭性得分 0.849? 結(jié)論基于非結(jié)節(jié)自動分類的 2D CNN 模型可以有效地對假陽性肺結(jié)節(jié)進(jìn)行剔除,相較于其他 2D CNN 具有競爭力,可為肺癌早期篩查提供幫助。

Objective In order to solve the problem of low sensitivity and a large number of false positives in the automatic detection of pulmonary nodules in CT images, this paper proposes a two-dimensional convolutional neural network ( CNN ) based on non-nodule automatic classification and applies it to the reduction of false positives in the detection of pulmonary nodules. Methods Firstly, the CT image was preprocessed by resampling and normalizing the original CT image, to solve the problem of inconsistent pixel spacing and image contrast of different samples. The positive samples were expanded by two-dimensional slice information collection in different spatial directions, and the negative samples were classified unsupervised to balance the positive and negative samples. Two-dimensional convolutional neural networks were trained with different types of negative samples and positive samples to obtain a number of 2D CNN pulmonary nodule detection models for reducing false-positive, by using the false positive reduction data set provided by LUNA16 to conduct 5-fold cross validation, and evaluated the model with the evaluation procedure provided by LUNA16. Results Compared with the model directly using a single 2D CNN for classification, the result of training multiple models after non nodule classification was better, the final CPM ( competition performance metric) competitive score was 0.849. Conclusions The 2D CNN model based on non-nodule automatic classification can effectively reduce the number of false-positive pulmonary nodules, which is competitive with other 2D CNN, and can provide help for early lung cancer screening.

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