Deep Learning-assisted Holographic Tomography for 3-D Cell Morphology Processing and Display

Han Yen Tu, Han Wen Chi, Tomoyoshi Shimobaba, Chau Jern Cheng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study proposes deep learning-enabled three-dimensional processing for segmentation of the inter-/intra-cell morphology with holographic tomography. We also employ computer-generated holography to demonstrate the observable cell morphology for 3D cell display and visualization.

Original languageEnglish
Title of host publicationFrontiers in Optics
Subtitle of host publicationProceedings Frontiers in Optics + Laser Science 2023, FiO, LS 2023
PublisherOptical Society of America
ISBN (Electronic)9781957171296
DOIs
Publication statusPublished - 2023
EventFrontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023 - Tacoma, United States
Duration: 2023 Oct 92023 Oct 12

Publication series

NameFrontiers in Optics: Proceedings Frontiers in Optics + Laser Science 2023, FiO, LS 2023

Conference

ConferenceFrontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023
Country/TerritoryUnited States
CityTacoma
Period2023/10/092023/10/12

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Space and Planetary Science
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • General Computer Science
  • Instrumentation

Fingerprint

Dive into the research topics of 'Deep Learning-assisted Holographic Tomography for 3-D Cell Morphology Processing and Display'. Together they form a unique fingerprint.

Cite this