Affiliation:
1. Korea Advanced Institute of Science and Technology (KAIST)
2. KAIST Institute for Health Science and Technology
3. Universität Stuttgart
4. Institute for Biochemistry
5. ETH Zürich
6. ScopeM (Scientific Center of Optical and Electron Microscopy)
7. Tomocube Inc.
Abstract
The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.
Funder
Young researchers’ exchange programme between Korea and Switzerland
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
National Research Foundation of Korea
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献