Gabor domain optical coherence microscopy

Rolland, Jannick P.; Meemon, Panomsak; Murali, Supraja; Jain, Aayush; Papp, Nicolene; Thompson, Kevin P.; Lee, Kye-Sung

doi:10.1117/12.849009

Cited by 6 publications

(4 citation statements)

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“…Due to larger number of spectra to be acquired in order to obtain spatial-resolved information, Raman imaging may exhibit reduced spectral resolution and lower intensity-over-noise ratios. 29,30 In large-area mapping using Raman imaging, additional challenges arise that can impact the quality and accuracy of the results. Difficulties in maintaining precise focus throughout the entire imaging process may arise, as focusing a large area uniformly can be challenging.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Electron backscatter diffraction (EBSD) provides crystallographic information but does not provide chemical information with the same level of specicity as Raman imaging. 30 Energy-dispersive X-ray spectroscopy (EDS) allows for elemental analysis but lacks the ability to differentiate between different molecular species. 35,36 Fourier transform infrared spectroscopy (FTIR) imaging, 37 on the other hand, utilizes infrared radiation to probe molecular vibrations but has lower spatial resolution compared to Raman imaging and is sensitive to water vapor.…”

Section: Introductionmentioning

confidence: 99%

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Statistical approaches to Raman imaging: principal component score mapping

Marin,

Bristol,

Rondinella

et al. 2024

Anal. Methods

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical approaches to Raman imaging: principal component score mapping

Marin,

Bristol,

Rondinella

et al. 2024

Anal. Methods

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“…24 Optical coherence microscopy (OCM) was introduced to achieve cellular resolution using a higher NA objective (i.e., ∼0.2) than conventional OCT (i.e., ∼0.04); the gain in resolution in OCM is reached at the expense of a limited depth of focus in the order of 100 to 200 μm. 25 Gabor-domain optical coherence microscopy (GD-OCM) was proposed by our group in 2008 to dynamically extend the imaging depth of OCM 26 and has since started to be adopted in other research groups as well. 27 A liquid lens is dynamically refocused at different depth locations to acquire multiple images that are then combined in a single volume.…”

Section: Introductionmentioning

confidence: 99%

Parallelized multi–graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy

et al. 2014

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Abstract. Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multigraphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000 × 1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1 × 1 × 0.6 mm 3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells

2020

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. 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 ( ) 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. Numerical methods were applied to compensate for the natural curvature of the cornea and produce an image of the flattened endothelium. A convolutional neural network (CNN) was trained to automatically delineate the cell boundaries using 180 manually annotated images from six corneas. Ten additional corneas were imaged with GDOCM and compared with specular microscopy (SM) to determine performance of the combined GDOCM and CNN to achieve automated endothelial counts relative to current procedural standards. Results: Cells could be imaged over a larger area with GDOCM than SM, and more cells could be delineated via automatic cell segmentation than via manual methods. ECD obtained from automatic cell segmentation of GDOCM images yielded a correlation of 0.94 ( ) with the manual segmentation on the same images, and correlation of 0.91 ( ) with the corresponding manually counted SM results. Conclusions: Automated endothelial cell counting on GDOCM images with large field of view eliminates selection bias and reduces sampling error, which both affect the gold standard of manual counting on SM images.

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Gabor domain optical coherence microscopy

Cited by 6 publications

References 0 publications

Statistical approaches to Raman imaging: principal component score mapping

Statistical approaches to Raman imaging: principal component score mapping

Parallelized multi–graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy

Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells

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