Your browser doesn't support javascript.
loading
Artificial intelligence-enabled quantitative phase imaging methods for life sciences.
Park, Juyeon; Bai, Bijie; Ryu, DongHun; Liu, Tairan; Lee, Chungha; Luo, Yi; Lee, Mahn Jae; Huang, Luzhe; Shin, Jeongwon; Zhang, Yijie; Ryu, Dongmin; Li, Yuzhu; Kim, Geon; Min, Hyun-Seok; Ozcan, Aydogan; Park, YongKeun.
Affiliation
  • Park J; Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Bai B; KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Ryu D; Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Liu T; Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Lee C; Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Luo Y; KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Lee MJ; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Huang L; Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Shin J; Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Zhang Y; KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Ryu D; Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Li Y; KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Kim G; Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Min HS; Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Ozcan A; KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Park Y; Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Nat Methods ; 20(11): 1645-1660, 2023 Nov.
Article in En | MEDLINE | ID: mdl-37872244
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
Subject(s)
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Biological Science Disciplines Language: En Journal: Nat Methods Journal subject: TECNICAS E PROCEDIMENTOS DE LABORATORIO Year: 2023 Document type: Article Country of publication: United States
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Biological Science Disciplines Language: En Journal: Nat Methods Journal subject: TECNICAS E PROCEDIMENTOS DE LABORATORIO Year: 2023 Document type: Article Country of publication: United States