Yang Xiaodu, He Dian, Li Yu, Li Chenyang, Wang Xinyue, Zhu Xingzheng, Sun Haitao, Xu Yingying
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, China.
Biomed Opt Express. 2024 Mar 19;15(4):2498-2516. doi: 10.1364/BOE.516541. eCollection 2024 Apr 1.
Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.
全面可视化和准确提取肿瘤血管对于研究胶质瘤的本质至关重要。如今，组织透明化技术能够在微米分辨率下对人类胶质瘤血管进行三维可视化，但当前的血管提取方案在实际条件下无法很好地应对具有高度破碎性和不规则性的复杂肿瘤血管的提取。在此，我们基于深度学习开发了一个名为FineVess的框架，用于在清除后的人类肿瘤组织的共聚焦显微镜图像中自动提取胶质瘤血管。在该框架中，设计了一个名为3D ResCBAM nnU-Net的定制深度学习网络来分割血管，并开发了一种基于预处理和后处理的新颖管道来自动优化分割结果。基于其在实际数据集上的应用，我们表明FineVess能够在具有挑战性的三维图像中高精度地提取可变和不完整的血管，优于其他传统和先进方案。对于提取的血管，我们计算了包括不同肿瘤分级的分形维数和血管壁完整性在内的血管形态特征，并通过定量分析验证了血管异质性。
