The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses.

中文翻译：

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WSA-MP-Net：用于光学分辨率光声显微镜中微血管提取的弱信号注意和多尺度感知网络

光学分辨率光声显微镜（OR-PAM）的独特优势是无需外源试剂即可实现高分辨率微血管成像。这种能力在组织微循环的研究中具有巨大的潜力。然而，跟踪和监测组织中的微血管形态和血流动力学具有挑战性，因为 OR-PAM 图像中的微血管分割由于血管结构的高密度、结构复杂性和低对比度而变得复杂。多年来已经开发了各种微脉管系统提取技术，但存在许多局限性：它们不能同时考虑粗血管和细血管分割，无法解决微脉管系统的不完整性和不连续性，缺乏基于深度学习算法的开放访问数据集。我们开发了一种新颖的分割方法，使用结合了弱信号注意机制和多尺度感知（WSA-MP-Net）模型的深度学习网络来提取 OR-PAM 图像中的血管分布。所提出的 WSA 网络专注于弱血管和微小血管，而 MP 模块则从不同血管尺寸中提取特征。此外，在网络输入输出数据的前后处理中加入Hessian矩阵增强，以增强血管的连续性。我们构建了正常血管（NV-ORPAM，660 个数据对）和肿瘤血管（TV-ORPAM，1168 个数据对）数据集来验证所提方法的性能。我们开发了一种半自动注释算法来获取网络优化的基本事实。我们成功地应用我们的优化模型来监测小鼠大脑中的胶质瘤血管生成，从而证明了我们的模型的可行性和出色的泛化能力。与以前的工作相比，我们提出的 WSA-MP-Net 提取了大量的微血管，同时保持血管连续性和信号保真度。在定量分析中，我们方法的指标值提高了约1.3%至25.9%。我们相信我们提出的方法提供了一种有前途的方法来提取 OR-PAM 的完整且连续的微血管网络，并使其能够在许多微血管相关的生物学研究和医学诊断中使用。