Publications by authors named "Thitiya Seesan"
Biomed Opt Express
May 2024
Article Synopsis
- A new deep-learning scatterer density estimator (SDE) was developed to analyze speckle patterns in optical coherence tomography (OCT) images and accurately estimate the density of scatterers.
- This SDE was trained on a large dataset of simulated OCT images that included a sophisticated noise model, accounting for shot noise, relative-intensity noise, and non-optical noise.
- Evaluations using scattering phantoms and tumor spheroids showed that the SDE significantly improved estimation accuracy compared to previous versions that used less effective noise models.
Biomed Opt Express
March 2024
Article Synopsis
- The text mentions a correction to an article found on page 168 of volume 13.
- The article is identified by its PubMed ID (PMID) 35154862.
- This correction likely addresses errors or updates that need to be noted for accuracy in the original publication.
Biomed Opt Express
January 2022
Article Synopsis
- Researchers developed a deep convolutional neural network (DCNN) to estimate key parameters such as tissue scatterer density, resolution, signal-to-noise ratio, and effective number of scatterers from optical coherence tomography (OCT) images.
- The DCNN was trained on a massive dataset of 1,280,000 digitally generated image patches and was validated both numerically and experimentally, showing high accuracy in its estimations.
- Experimental results indicated that the model could effectively measure scatterer density in scattering phantoms and even demonstrated its application in monitoring changes in a tumor cell spheroid during cell necrosis.
