光声计算层析成像中的皮肤智能去除方法  

Intelligent Skin-Removal for Photoacoustic Computed Tomography

作　　者：袁怡鑫 陈涛 刘成波[2] 孟静[1] Yuan Yixin;Chen Tao;Liu Chengbo;Meng Jing(School of Computer,Qufu Normal University,Rizhao 276826,Shandong,China;Institute of Biomedical and Health Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Guangdong,China)

机构地区：[1]曲阜师范大学计算机学院,山东日照276826 [2]中国科学院深圳先进技术研究院生物医学与健康工程研究所,广东深圳518055

出　　处：《中国激光》2023年第21期134-142,共9页Chinese Journal of Lasers

基　　金：山东省自然科学基金(ZR2020MF105);广东省生物医学光学重点实验室开放课题(2020B121201010)。

摘　　要：光声计算层析成像(PACT)不需要外源性对比剂便可获取厘米级深度的光声图像。然而,来自皮肤的高强度光声信号遮盖了皮下深层组织信息,阻碍了感兴趣区域光声图像的正面显示和分析。因此,笔者提出了融合多尺度感知和残差连接的U型深度学习模型,并采用该模型实现了PACT光声图像中皮肤信号的智能分割。首先,提出以单类皮肤区域标注为基准标签图像的非像素级皮肤区域标注方法,该方法不需要像素级图像标注,能够显著降低数据处理的复杂度;然后,设计了皮肤完整性拟合和皮肤掩膜生成算法,并采用该算法实现了PACT图像中皮肤信号的自动去除。使用PACT成像实验获得的人体腿部外周血管光声图像验证了所提方法在皮肤组织高精度智能提取和去除方面的正确性和有效性。与现有的皮肤去除工作相比,本文所提皮肤去除算法对MAP图像的重建误差下降了50%~70%,峰值信噪比平均提升了约4.5 dB,为深层组织PACT图像的高清晰显示提供了一条有效途径。Objective Photoacoustic computed tomography(PACT)is an important photoacoustic imaging modality.Compared with photoacoustic microscopy,PACT can detect biological tissues located several centimeters deep without external contrast agents.Equipped with a multi-channel data acquisition card,PACT has the potential for high-speed imaging under a large field of view and is currently used in clinical and preclinical applications,such as whole-body imaging of small animals and human organs.However,skin tissue contains a lot of melanin,and the high-intensity photoacoustic signal from the skin covers the deep subcutaneous tissue information during the imaging process,hindering the en-face display and analysis of the photoacoustic image of the region of interest.Existing works have successfully removed most of the skin signals in photoacoustic images,but there are still some existing problems:(1)most of them are based on photoacoustic microscopic images of shallow tissues or directly extracted vascular structures in the background;the skin removal of deep tissue PACT images has not been reported;(2)the current pixel-level manual labeling takes a lot of time,and there are shortcomings of low extraction accuracy and low efficiency;(3)owing to reconstruction artifacts and changes in light intensity,the signal amplitudes of the skin area are uneven,and there exists many small segments that cannot be distinguished from the background,which increases the difficulty of extracting a complete and continuous skin signal.Methods Considering the continuity of the skin tissue and the uniformity of the thickness of the local imaging area,this study proposes a U-shaped deep learning(DL)model that combines multi-scale perception and a residual structure(MD-ResUnet)to automatically remove skin areas in PACT deep tissue photoacoustic images.The introduction of the residual structure in this model can integrate low-and high-level feature information to prevent model degradation,and the multi-scale dilated convolution blocks can increase the co

关 键 词：生物光学 光声成像 皮肤分割 深度学习 外周血管成像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]