PDF(Pubmed)
Abstract：
Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function.
摘要：
介观光声成像（PAI）可以实现组织中血管网络的无标记可视化，具有高对比度和分辨率。从3DPAI数据中分割这些网络并解释其生理和病理意义是至关重要的，但由于当前方法的耗时和易错性质，因此具有挑战性。深度学习提供了一个潜在的解决方案;然而，监督分析框架通常需要人工注释的地面实况标签。为了解决这个问题，引入了一种无监督的图像到图像翻译深度学习模型，船舶分段生成对抗网络(VAN-GAN)。VAN-GAN将与现实生活解剖结构非常相似的合成血管网络集成到其训练过程中，并学习复制PAI系统的基础物理原理，以学习如何从3D光声图像中分割脉管系统。适用于各种各样的计算机模拟，在体外，和体内数据，包括患者来源的乳腺癌异种移植模型和3D临床血管造影,VAN-GAN展示了其促进3D血管网络的准确和无偏分割的能力。通过利用合成数据，VAN-GAN减少了对手动标签的依赖，从而降低了进入高质量血管分割的门槛(F1评分：VAN-GANvs.U-Net=0.84vs.0.87），并加强对血管结构和功能的临床前和临床研究。
