北京生物醫(yī)學(xué)工程

基于Transformer的血管內(nèi)超聲圖像分割

Transformer-based intravascular ultrasound image segmentation

作者：李佳松曹洪帥舒麗霞藺嫦燕

單位：首都醫(yī)科大學(xué)附屬北京安貞醫(yī)院(北京 100029)   首都醫(yī)科大學(xué)臨床生物力學(xué)應(yīng)用基礎(chǔ)研究所北京市重點(diǎn)實(shí)驗(yàn)室（北京 100069）<br />通訊作者：藺嫦燕。E-mail: [email protected]

關(guān)鍵詞：血管內(nèi)超聲圖像；深度學(xué)習(xí)；Transformer；分割；鈣化

分類號：R318.04

出版年·卷·期（頁碼）：2023·42·1（16-20）

摘要：

目的提出一種基于Transformer的血管內(nèi)超聲圖像分割方法，以解決冠狀動脈鈣化病變血管內(nèi)超聲圖像顯影不完全導(dǎo)致的分割管腔、外彈力膜和鈣化斑塊精度不高的問題。方法采用深度學(xué)習(xí)方法，在UNet結(jié)構(gòu)的基礎(chǔ)上用多分辨率卷積層提取不同大小類別特征，在特征編碼模塊與特征解碼模塊之間使用Transformer聯(lián)系上下文信息，同時分割管腔、外彈力膜和鈣化斑塊。最后以34個40 MHz血管內(nèi)超聲序列得到的訓(xùn)練集720張和測試集240張為例對上述方法進(jìn)行訓(xùn)練和測試。結(jié)果外彈力膜分割杰卡德系數(shù) (Jaccard index，JI)為0.92，豪斯多夫距離（Hausdorff distance，HD)為0.84 mm；管腔分割JI為0.85， HD為1.44 mm；鈣化分割JI為0.67， HD為0.68 mm。結(jié)論該方法能夠提升血管內(nèi)超聲圖像的分割精度，并且在鈣化病變血管顯影不完全時能夠保持分割效果。

Objective To propose a intravascular ultrasound image segmentation method based on Transformer，and to solve the problem of low precision in segmenting of lumen, external elastic membrane and calcified plaque caused by incomplete development of intravascular ultrasound image of coronary artery calcification disease. Methods A deep learning technique was employed to extract different size category features using multi-resolution convolutional layers based on UNet structure. Additionally, Transformer was used to link contextual information between feature encoding module and feature decoding module. For training and testing the above method, 34 40 MHz intravascular ultrasound sequences were captured, along with 720 training sets and 240 test sets. Results External elastic membrane segmentation JI was 0.92，HD was 0.84 mm；lumen segmentation JI was 0.85，HD was 1.44 mm；calcification segmentation JI was 0.67，HD was 0.68 mm. Conclusions The method can improve the segmentation accuracy of intravascular ultrasound images and maintain the segmentation effect when the development of calcified lesion vessels is incomplete.

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