北京生物醫(yī)學(xué)工程

基于三維卷積神經(jīng)網(wǎng)絡(luò)的頸部ＴＯＦＭＲＡ圖像的血管自動分割

Automatic vessel segmentation of neck TOF MRA image based on 3D convolutional neural network

作者：邱偉陳碩魏寒宇李睿

單位：清華大學(xué)醫(yī)學(xué)院生物醫(yī)學(xué)工程系生物醫(yī)學(xué)影像研究中心（北京 100084）。 <p>通信作者：李睿，博士，研究員。E-mail：[email protected]</p>

關(guān)鍵詞：動脈粥樣硬化；卷積神經(jīng)網(wǎng)絡(luò)；時間飛躍法磁共振血管造影圖像；三維切塊；血管分割

分類號：R318.04

出版年·卷·期（頁碼）：2022·41·6（551-557）

摘要：

目的提出一種基于三維卷積神經(jīng)網(wǎng)絡(luò)（convolutional neural network, CNN）的分割模型，通過設(shè)計網(wǎng)絡(luò)結(jié)構(gòu)和調(diào)整參數(shù)，實(shí)現(xiàn)對頸部時間飛躍法磁共振血管造影（time of flight magnetic resonance angiography, TOF MRA）圖像的自動精準(zhǔn)血管分割。方法三維頸部TOF MRA圖像數(shù)據(jù)來源于一項(xiàng)無癥狀老年人心腦血管病發(fā)病風(fēng)險研究中的166例受試者。首先由血管影像臨床經(jīng)驗(yàn)的放射診斷醫(yī)生運(yùn)用Mimics軟件對頸部動脈血管進(jìn)行手動標(biāo)注，再按照8:1:1的比例將數(shù)據(jù)隨機(jī)分為訓(xùn)練集（132例）、驗(yàn)證集（17例）和測試集（17例）。針對TOF MRA圖像稀疏性和高對比度的特點(diǎn)，采用3D CNN的優(yōu)化模型，通過在U-Net網(wǎng)絡(luò)的編碼和解碼路徑中多層級的加入專門的模塊和不同分辨率的原始圖像，更好地學(xué)習(xí)和利用到圖像的獨(dú)有特征。為研究輸入圖像的大小對網(wǎng)絡(luò)性能的影響，從原始圖像中分別裁剪出3種不同尺寸大小的三維切塊來進(jìn)行模型訓(xùn)練。在十折交叉驗(yàn)證下，采用Dice系數(shù)的平均值和標(biāo)準(zhǔn)差，以及靈敏度和特異度對模型的分割性能進(jìn)行評價。采用單因素方差分析對比3種切塊尺寸下的測試實(shí)驗(yàn)。結(jié)果所提出的新模型取得了最高的分割Dice值（0.932 0）、靈敏度（0.918 6）和特異度（0.999 6），以及最小的Dice值標(biāo)準(zhǔn)差（0.005 1）。ANOVA顯著性水平為，表明了不同切塊尺寸下的模型分割結(jié)果有顯著性差異，尺寸增大，模型分割結(jié)果更好。結(jié)論所提出的基于3D CNN的優(yōu)化模型在TOF MRA圖像血管自動精準(zhǔn)分割上優(yōu)于已有方法。此外，增加模型輸入圖像的尺寸大小有助于提高分割性能。

Objective Propose an automatic segmentation model based on 3D convolutional neural network to segment neck vessel from the time-of-flight magnetic resonance angiography(TOF MRA) data. Methods A total of 166 neck TOF MRA images were selected from the Cardiovascular Risk of Old Population(CROP) study. Vessel segmentation labels were manually delineated by vascular imaging professionals in 3D TOF images (Materialise Mimics, Mimics Medical 17.0), and were subsequently examined by experienced imaging experts. Randomly, 132 images were assigned into training dataset, 17 images were assigned into validating dataset, 17 images were assigned into test dataset according to a ratio of 8∶1∶1. Based on the sparsity and high contrast of TOF MRA image, a 3D CNN model suitable for TOF MRA data were proposed by adding special modules and images to the encoding and decoding path of U-Net network. The unique features of the image can be better learned and utilized. In addition, in order to study the influence of the size of the input on the network performance, three different sizes of 3D patch randomly cropped from the original image were selected for model training and predicting. The average Dice coefficients, sensitivity, specificity and standard deviation of the Dice coefficients under 10-fold cross-validation were used to evaluate the segmentation performance of the model. In this comparative experiments, One-way ANOVA was carried out for the experiments of the three kinds of patch size. Results Proposed 3D CNN model obtained the highest segmentation Dice coefficient value(0.932 0), sensitivity(0.918 6) and specificity(0.999 6), and the smallest standard deviation (0.005 1). In the results of One-way ANOVA, the p<0.01, indicated that the results of different patch sizes had significant differences. And as the size increased, the segmentation result of the model was better. Conclusions The experimental results showed that the proposed model can automatically segment the neck vascular from TOF-MRA volumes and outperformed the state of the art. Besides, increasing the size of the input image can improve the segmentation performance.

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