北京生物醫(yī)學(xué)工程

基于水平集方法的冠狀動脈CT圖像分割

Coronary CT image segmentation based on level set method

作者：黃山程曉光

單位：北京積水潭醫(yī)院放射科(北京 100035)

關(guān)鍵詞：水平集方法; CT圖像; 冠脈識別與分割; FFRct; 臨床應(yīng)用

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·6（569-573）

摘要：

目的采用水平集方法實現(xiàn)對CT圖像中冠狀動脈的高效自動識別與分割，借此簡化三維模型重建工作，從而提高FFRct整體的計算效率。方法選取20名冠心病患者作為研究對象，提取26支狹窄冠脈血管的CT圖像。分別利用水平集方法和手動方法對CT圖像進行冠脈的識別與分割處理從而生成三維模型。利用CFD仿真方法分別獲取各冠脈段的FFR數(shù)值，并通過Bland-Altman方法分析兩種方法所得FFR的一致性，用以驗證水平集方法在冠脈識別精度上與手動方法水平的差異。結(jié)果對比了獲取FFR數(shù)值兩種冠脈識別方法整體所需的時間。與手動方法相比，水平集方法的模型重建時間減少了10倍以上。經(jīng)檢驗，兩種冠脈識別方法的FFR數(shù)值計算結(jié)果一致性良好。結(jié)論基于水平集方法可以實現(xiàn)CT圖像冠脈的高效自動識別，具有一定的臨床應(yīng)用意義。

Objective The level set method is used to realize efficient automatic identification and segmentation of coronary arteries in CT images, thereby simplifying the reconstruction of the three-dimensional model and improving the overall calculation efficiency of FFRct. Methods 20 patients with coronary heart disease were selected as the research objects, and CT images of 26 narrow coronary vessels were extracted. The level set method and manual method are used to identify and segment the coronary artery on CT images to generate a three-dimensional model. The CFD simulation method was used to obtain the FFR value of each coronary artery segment, and the consistency of the FFR obtained by the two methods was analyzed by the Bland-Altman method to verify the difference between the level set method in the coronary recognition accuracy and the manual method. Results The overall time required to obtain the FFR value of the two coronary artery identification methods was compared. Compared with the manual method, the model reconstruction time of the level set method is reduced by more than 10 times. After testing, the FFR numerical calculation results of the two coronary artery identification methods are in good agreement. Conclusions The level set method can realize the high-efficiency automatic identification of coronary artery in CT images, which has certain clinical application significance.

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