Yuan Alan Yilun, Gao Yang, Peng Liangliang, Zhou Lingxiao, Liu Jun, Zhu Siwei, Song Wei
Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
These authors contributed equally to this work.
Biomed Opt Express. 2020 Oct 16;11(11):6445-6457. doi: 10.1364/BOE.409246. eCollection 2020 Nov 1.
Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.
由于光声（PA）技术能够识别分子特异性并实现高达细胞水平的光学衍射极限横向分辨率，因此已广泛应用于血管成像。血管图像携带重要的医学信息，为专业诊断提供指导。现代图像处理技术对血管分割有很大贡献。然而，这些方法存在分割不足或过度分割的问题。因此，我们展示了采用全卷积网络和U-net的结果，并提出了一种将两者结合应用于PA血管图像的混合网络。比较结果表明，混合网络可以显著提高分割的准确性和鲁棒性。
