Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.

Journal: Journal of biomedical optics

PMID: 39247058

Abstract

SIGNIFICANCE: Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time.

Authors

Thomas T Livecchi

Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey, United States.
Steven L Jacques

University of Washington, Department of Bioengineering, Seattle, Washington, United States.
Hrebesh M Subhash

Colgate-Palmolive Company, Global Technology and Design Center, Piscataway, New Jersey, United States.
Mark C Pierce

Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey, United States.

Keywords

Algorithms Deep Learning Fingers Hemoglobins Humans Hyperspectral Imaging Image Processing, Computer-Assisted Melanins Monte Carlo Method Neural Networks, Computer Scattering, Radiation

External Resources

View on PubMed Access via DOI PubMed (39247058)

Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals