Ly Cao Duong, Nguyen Van Tu, Vo Tan Hung, Mondal Sudip, Park Sumin, Choi Jaeyeop, Vu Thi Thu Ha, Kim Chang-Seok, Oh Junghwan
Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
New-senior Healthcare Innovation Center (BK21 Plus), Pukyong National University, Busan 48513, Republic of Korea.
Photoacoustics. 2021 Oct 20;25:100310. doi: 10.1016/j.pacs.2021.100310. eCollection 2022 Mar.
Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.
光声（PA）显微镜能够基于光吸收对比度和空间超声分辨率对柔软的生物组织进行成像。PA成像的主要应用之一是对微血管系统进行表征。然而，来自皮肤层的强烈PA信号掩盖了皮下血管，导致在人体研究中只能间接重建PA图像。针对这一现状，我们研究了一种深度学习（DL）自动算法，以实现高分辨率和高对比度分割，从而拓宽PA成像的应用范围。在本研究中，我们提出了一种基于改进型U-Net的DL模型，用于提取来自皮肤和深层血管的生成PA信号幅度之间的关系特征。本研究说明了混合复杂网络作为体内PA成像自动分割工具的更广泛潜力。通过注入DL的解决方案，我们的结果优于之前的研究，在大尺寸高分辨率PA图像上实现了实时语义分割。
