Our retinal vessel segmentation approach utilizes deep hierarchical semantic segmentation along with a closing operation. From fundus images, the retinal vessels are extracted using the supervised learning segmentation algorithm. Deep semantic segmentation that provides a hierarchical solution is adopted for rough retinal vessel segmentation. The rough segmentation results are then processed by a closing operation to refine the segmented retinal vessels. We performed experiments and comparisons with ground truths to evaluate the qualitative and quantitative effectiveness of our method. Our method effectively segments retinal vessels, as demonstrated by the experimental results.
KEYWORDS: Angiography, Image resolution, Image filtering, Gaussian filters, Current controlled current source, Signal to noise ratio, Digital filtering, Scientific research, Image quality, Denoising
Although fundus fluorescein angiography is an imaging modality that supports ophthalmic diagnosis, it requires the intravenous injection of harmful fluorescein dye. We propose the synthesis of fluorescein angiography images from fundus structure images to avoid injection. Specifically, we automatically synthesize high-resolution fundus fluorescein angiography images through an algorithm that integrates a generative adversarial networks and image stitching and enhancement. By evaluating the peak signal-to-noise ratio and structural similarity index of the proposed algorithm, pix2pix, and cycleGAN, we confirmed the superior performance of our proposal. To further validate the proposed algorithm, we compared the fundus fluorescein angiography images synthesized by our algorithm, pix2pix, and cycleGAN. The experimental results show that our algorithm provides the highest resolution and quality in the synthesis of fluorescein angiography images from fundus structure images among the evaluated methods.
Most glaucoma surgeries involve creating new aqueous outflow pathways with the use of a small surgical instrument. This article reported a microscope-integrated, real-time, high-speed, swept-source optical coherence tomography system (SS-OCT) with a 1310-nm light source for glaucoma surgery. A special mechanism was designed to produce an adjustable system suitable for use in surgery. A two-graphic processing unit architecture was used to speed up the data processing and real-time volumetric rendering. The position of the surgical instrument can be monitored and measured using the microscope and a grid-inserted image of the SS-OCT. Finally, experiments were simulated to assess the effectiveness of this integrated system. Experimental results show that this system is a suitable positioning tool for glaucoma surgery.
A compact, high-speed line scanning quasi-confocal ophthalmoscope (LSO) for retina imaging is presented in this paper. By using a line beam to illuminate the retina, meanwhile a linear array sensor is used for imaging the retina, the LSO system significantly reduces the size, complexity, and cost comparing to a conventional confocal scanning laser ophthalmoscope (CSLO). With only one moving scanner to provide raster scanning of the line beam of the retina, the imaging frequency achieves 160 Hz and the lateral resolution is nearly 10 μm for 1024×330 pixels imaging mode. Preliminary experiments are performed for imaging the macula, the optic nerve head and other targets, providing high resolution and high speed videos of human retina.
Adaptive optics is implemented in a confocal scanning fluorescence microscope with wavefront sensorless scheme. Using the image sharpness as the optimization metric, aberration correction is performed to compensate both system- and specimen-induced aberrations by using stochastic parallel gradient descent algorithm based upon Zernike polynomial modes. In vivo vascular imaging of mice ear is completed and the results revealed the improved signal and resolution leading to in substantially enhanced image contrast with aberration correction which allowed us to detect clearer vasculature structures.
We have demonstrated adaptive correction of specimen-induced aberration during in vivo imaging of mouse bone marrow vasculature with confocal fluorescence microscopy. Adaptive optics system was completed with wavefront sensorless correction scheme based on stochastic parallel gradient descent algorithm. Using image sharpness as the optimization metric, aberration correction was performed based upon Zernike polynomial modes. The experimental results revealed the improved signal and resolution leading to a substantially enhanced image contrast with aberration correction. The image quality of vessels at 38- and 75-μm depth increased three times and two times, respectively. The corrections allowed us to detect clearer bone marrow vasculature structures at greater contrast and improve the signal-to-noise ratio.
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