ORCID Profile
0000-0003-0156-0117
Current Organisations
American International University-Bangladesh
,
Murdoch University
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Publisher: Foundation of Computer Science
Date: 17-12-2018
Publisher: MECS Publisher
Date: 08-12-2018
Publisher: IEEE
Date: 02-2020
Publisher: IEEE
Date: 12-2017
Publisher: Foundation of Computer Science
Date: 17-05-2018
Publisher: IEEE
Date: 07-2019
Publisher: Foundation of Computer Science
Date: 15-03-2017
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 31-03-2023
DOI: 10.1038/S41526-023-00269-0
Abstract: Spaceflight associated neuro-ocular syndrome (SANS) is common amongst astronauts on long duration space missions and is associated with signs consistent with elevated cerebrospinal fluid (CSF) pressure. Additionally, CSF pressure has been found to be elevated in a significant proportion of astronauts in whom lumbar puncture was performed after successful mission completion. We have developed a retinal photoplethysmographic technique to measure retinal vein pulsation litudes. This technique has enabled the development of a non-invasive CSF pressure measurement apparatus. We tested the system on healthy volunteers in the sitting and supine posture to mimic the range of tilt table extremes and estimated the induced CSF pressure change using measurements from the CSF hydrostatic indifferent point. We found a significant relationship between pulsation litude change and estimated CSF pressure change ( p 0.0001) across a range from 2.7 to 7.1 mmHg. The increase in pulse litude was highest in the sitting posture with greater estimated CSF pressure increase ( p 0.0001), in keeping with physiologically predicted CSF pressure response. This technique may be useful for non-invasive measurement of CSF pressure fluctuations during long-term space voyages.
Publisher: Elsevier BV
Date: 2022
DOI: 10.1016/J.OPTOM.2022.11.001
Abstract: Retinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV). MSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels. MSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively. The results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.
Publisher: IEEE
Date: 17-12-2020
Publisher: Springer International Publishing
Date: 31-12-2017
Publisher: IEEE
Date: 12-2017
Publisher: MECS Publisher
Date: 08-12-2018
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 12-2017
No related grants have been discovered for AZM Ehtesham Chowdhury.