Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images

An accurate and universal approach for short-exposure-time microscopy image enhancement

Fluorescence microscopy imaging has become an essential technique in the biology and biomedical science which can provide comprehensive visualization of many biological processes, and the exposure time is one of the most critical parameters for fluorescence microscopy imaging. Short-exposure-time (SET) imaging overcomes the limitations of photo-bleaching and photo-toxicity, allowing comprehensive visualization of the biological processes. Unfortunately, SET images deteriorate the signal to noise ratio and the image quality when compared with the long-exposure-time (LET) images. Therefore, we introduce a data-driven microscopy image enhancement network (MIEN) to improve the quality of SET microscopy images without requiring any manual intervention, facilitating the production of high-resolution and high contrast images. The universal property and accuracy of the proposed network are validated on 38,500 real fluorescence microscopy images, which contain different object contents and are collected from various exposure time ratios and fluorescence microscopes platforms. Experimental results demonstrate that the proposed MIEN model is effective to enhance the quality of SET fluorescence microscopy images, and can be used to observe delicate changes in cells, tissues and organs with low photo-bleaching and photo-toxicity.

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.

Quantitative Label-Free Imaging of 3D Vascular Networks Self-Assembled in Synthetic Hydrogels

Vascularization is an important strategy to overcome diffusion limits and enable the formation of complex, physiologically relevant engineered tissues and organoids. Self-assembly is a technique to generate in vitro vascular networks, but engineering the necessary network morphology and function remains challenging. Here, autofluorescence multiphoton microscopy (aMPM), a label-free imaging technique, is used to quantitatively evaluate in vitro vascular network morphology. Vascular networks are generated using human embryonic stem cell-derived endothelial cells and primary human pericytes encapsulated in synthetic poly(ethylene glycol)-based hydrogels. Two custom-built bioreactors are used to generate distinct fluid flow patterns during vascular network formation: recirculating flow or continuous flow. aMPM is used to image these 3D vascular networks without the need for fixation, labels, or dyes. Image processing and analysis algorithms are developed to extract quantitative morphological parameters from these label-free images. It is observed with aMPM that both bioreactors promote formation of vascular networks with lower network anisotropy compared to static conditions, and the continuous flow bioreactor induces more branch points compared to static conditions. Importantly, these results agree with trends observed with immunocytochemistry. These studies demonstrate that aMPM allows label-free monitoring of vascular network morphology to streamline optimization of growth conditions and provide quality control of engineered tissues.

Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visualize both the perfused and the non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here, we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopic images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov random field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method outperforms both state-of-the-art method, specific to these data, as well as more classical vasculature segmentation methods.