Abstract
Rationale: Vascular morphological features reflect the vessel biological functions and analysis of these features is critical for understanding physiological and pathological process of vascular development and vascular diseases. Mouse retinal vasculature is a well-recognized and commonly used animal model for angiogenesis and microvascular remodeling. Thus, a powerful tool to analyze morphological changes of retinal vasculature is in urgent need. Objective: Develop a comprehensive and high accurate pipeline to analyze morphological changes of mouse retinal vasculature. Methods and Results: Retinas from postnatal mice at P2-P7 are harvested and retinal vasculatures are visualized by isolectin B4 whole mount staining. A comprehensive software, Vessel Tech, is developed to perform vessel segmentation, network reconstruction and analysis. Vessel Tech relies on recent advancements in the convolutional neural network and techniques from network data analysis to provide high accuracy vascular identification and comprehensive vascular morphological feature extraction. Compared with existing blood vessel analysis softwares, Vessel Tech shows great improvement in accuracy. Based on the reconstructed vessel network, various metrics of angiogenesis and micro-remodeling are extracted and statistically analyzed. Conclusions: We generated and verified a novel pipeline, named “Vessel Tech”, which could automatically process retinal vascular images, reconstruct vessel network with high accuracy and assay global and local vascular characteristics. The development of Vessel Tech provides a powerful tool to precisely study the physiological and pathological variations during vascular development and vascular diseases.
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