A comprehensive understanding of microvascular networks is required to generate platforms that track treatment efficacy. Current approaches are labor intensive and limited to small tissue volumes. In this work we describe an acquisition and segmentation framework for low-cost imaging and microvascular modeling at resolution comparable to confocal with data rates comparable to light-sheet microscopy. Segmentation is performed using a GPU-based method that extracts microvascular structure and connectivity embedded in these images. The microvasculature network is stored such that the graph [G = V, E] structure can be exploited to quantify large-scale angiomes and facilitate data mining at the terabyte scale.
Deep ultraviolet light excites many common fluorophores and is heavily absorbed by biological samples, making it ideal for facial histology of fresh biological samples. Microscopy with ultraviolet surface excitation (MUSE) provides high-resolution diagnostic images comparable to slide-based imaging. However, current implementations of MUSE are limited to the facial surface of the sample, restricting the availability of three-dimensional tissue structure. Our work extends MUSE imaging by providing inexpensive three dimensional images at resolutions comparable to confocal microscopy with the throughput of wide-field imaging. This approach allows comprehensive imaging of macro-scale samples by eliminating constraints on sample size and imaging depth. In this paper, we discuss recent advances in milling with ultraviolet excitation, with a focus on complex neurological structures such as cellular and microvascular networks.
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