skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology.
Title: Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology.
The rapid accessibility of portable and affordable retinal imaging devices has made early differential diagnosis easier. For example, color funduscopy imaging is readily available in remote villages, which can help to identify diseases like age-related macular degeneration (AMD), glaucoma, or pathological myopia (PM). On the other hand, astronauts at the International Space Station utilize this camera for identifying spaceflight-associated neuro-ocular syndrome (SANS). However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e.g., SANS) for more precise disease identification. Moreover, due to low bandwidth limits, the imaging data has to be compressed for transfer between these two places. Different super-resolution algorithms have been proposed throughout the years to address this. Furthermore, with the advent of deep learning, the field has advanced so much that 2 and 4 compressed images can be decompressed to their original form without losing spatial information. In this paper, we introduce a novel model called Swin-FSR that utilizes Swin Transformer with spatial and depth-wise attention for fundus image super-resolution. Our architecture achieves Peak signal-to-noise-ratio (PSNR) of 47.89, 49.00 and 45.32 on three public datasets, namely iChallenge-AMD, iChallenge-PM, and G1020. Additionally, we tested the model’s effectiveness on a privately held dataset for SANS and achieved comparable results against previous architectures.  more » « less
Award ID(s):
2201599
PAR ID:
10522967
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
MICCAI, Springer
Date Published:
ISBN:
978-3-031-43990-2
Format(s):
Medium: X
Location:
Vancouver, BC, Canada
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Nano Convergence)
    Abstract Single-molecule localization microscopy (SMLM) breaks the optical diffraction limit by numerically localizing sparse fluorescence emitters to achieve super-resolution imaging. Spectroscopic SMLM or sSMLM further allows simultaneous spectroscopy and super-resolution imaging of fluorescence molecules. Hence, sSMLM can extract spectral features with single-molecule sensitivity, higher precision, and higher multiplexity than traditional multicolor microscopy modalities. These new capabilities enabled advanced multiplexed and functional cellular imaging applications. While sSMLM suffers from reduced spatial precision compared to conventional SMLM due to splitting photons to form spatial and spectral images, several methods have been reported to mitigate these weaknesses through innovative optical design and image processing techniques. This review summarizes the recent progress in sSMLM, its applications, and our perspective on future work. Graphical Abstract 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al (, npj Digital Medicine)
    Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classi cation approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA signi cantly outperformed a single-image baseline in 19/20 head-to- head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most in uential, prior imaging still provides additional prognostic value. 
    more » « less
  3. ; ; (, ACM Transactions on Spatial Algorithms and Systems)
    Aref, Walid G. (Ed.)
    The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. In order to enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this paper, we introduce theSTARsystem.STARis a distributed in-memory data stream warehouse system that provides low-latency and up-to-date analytical results over a fast-arriving spatial data stream.STARsupports both snapshot and continuous queries that are composed of aggregate functions and ad hoc query constraints over spatial, textual, and temporal data attributes.STARimplements a cache-based mechanism to facilitate the processing of snapshot queries that collectively utilizes the techniques of query-based caching (i.e., view materialization) and object-based caching. Moreover, to speed-up processing continuous queries,STARproposes a novel index structure that achieves high efficiency in both object checking and result updating. Extensive experiments over real data sets demonstrate the superior performance ofSTARover existing systems. 
    more » « less
  4. ; ; (, OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP))
    We propose an end-to-end optimized adversarial deep compressed imaging modality. This method exploits the adversarial duality of the sensing basis and sparse representation basis in compressed sensing framework and shows solid super-resolution results. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; (, Brain Structure and Function)
    We investigated the impact of age-related macular degeneration (AMD) on visual acuity and the visual white matter. We combined an adaptive cortical atlas and diffusion-weighted magnetic resonance imaging (dMRI) and tractography to separate optic radiation (OR) projections to different retinal eccentricities in human primary visual cortex. We exploited the known anatomical organization of the OR and clinically relevant data to segment the OR into three primary components projecting to fovea, mid- and far-periphery. We measured white matter tissue properties—fractional anisotropy, linearity, planarity, sphericity—along the aforementioned three components of the optic radiation to compare AMD patients and controls. We found differences in white matter properties specific to OR white matter fascicles projecting to primary visual cortex locations corresponding to the location of retinal damage (fovea). Additionally, we show that the magnitude of white matter properties in AMD patients’ correlates with visual acuity. In sum, we demonstrate a specific relation between visual loss, anatomical location of retinal damage and white matter damage in AMD patients. Importantly, we demonstrate that these changes are so profound that can be detected using magnetic resonance imaging data with clinical resolution. The conserved mapping between retinal and white matter damage suggests that retinal neurodegeneration might be a primary cause of white matter degeneration in AMD patients. The results highlight the impact of eye disease on brain tissue, a process that may become an important target to monitor during the course of treatment. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email