Significance: An accurate, automated, and unbiased cell counting procedure is needed for tissue selection for corneal transplantation.
Aim: To improve accuracy and reduce bias in endothelial cell density (ECD) quantification by combining Gabor-domain optical coherence microscopy (GDOCM) for three-dimensional, wide field-of-view (1 mm2) corneal imaging and machine learning for automatic delineation of endothelial cell boundaries.
Approach: Human corneas stored in viewing chambers were imaged over a wide field-of-view with GDOCM without contacting the specimens.
Positron emission tomography-magnetic resonance (PET-MR) hybrid imaging is a relatively new imaging modality combining the superb MR contrast capabilities among different soft-tissue structures with the high sensitivity of PET functional imaging. With the development of any new technology, a variety of limitations will be encountered including the introduction of new types of artifacts. In this case report, we present a restaging PET-MR scan for multiple myeloma that showed severely decreased fluorodeoxyglucose activity in the liver on the PET attenuated corrected images.
View Article and Find Full Text PDFGabor-domain optical coherence microscopy (GDOCM) demonstrated corneal imaging with cellular resolution and differentiation in mice over a field of view of 1 mm. Contact and non-contact imaging was conducted on six healthy and six hyperglycemic C57BL/6J mice. Cellular resolution in the 3D GDOCM images was achieved after motion correction.
View Article and Find Full Text PDFWe report on a pathway for Gabor domain optical coherence microscopy (GD-OCM)-based metrology to assess the donor’s corneal endothelial layers ex vivo. Six corneas from the Lions Eye Bank at Albany and Rochester were imaged with GD-OCM. The raw 3-D images of the curved corneas were flattened using custom software to enhance the 2-D visualization of endothelial cells (ECs); then the ECs within a circle of 500-μm-diameter were analyzed using a custom corner method and a cell counting plugin in ImageJ.
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