High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering
Open accessAbstract
Hyperspectral stimulated Raman scattering (SRS) by spectral focusing can generate label-free chemical images through temporal scanning of chirped femtosecond pulses. Yet, pulse chirping decreases the pulse peak power and temporal scanning increases the acquisition time, resulting in a much slower imaging speed compared to single-frame SRS using femtosecond pulses. In this paper, we present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-frame femtosecond SRS image. Our DenseNet-based learning method, termed as DeepChem, achieves high-speed chemical imaging with a large signal level. Speed is improved by 2 orders of magnitude with four subcellular components (lipid droplet, endoplasmic reticulum, nuclei, cytoplasm) classified in MIA PaCa-2 cells and other cell types which were not used for training. Lipid droplet dynamics and cellular response to dithiothreitol in live MIA PaCa-2 cells are demonstrated using this computationally multiplex method.
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The unsupervised autoencoder models do not utilize any ground truth information during training, in contrast with previous supervised machine learning approaches for Raman cell imaging.
“Finally, we remark that our unsupervised autoencoder models do not utilize any ground truth information during training, in contrast with previous supervised machine learning approaches for Raman cell imaging and characterization where specific target concentrations are used as labels to train the model (e.g., using separately acquired fluorescence images) (90–93).”
From Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders (2024) · cites this paper as [91]
The indexed text (section labeled Abstract; no page given) says DeepChem was trained on paired Raman images and segmented subcellular-organelle maps, supporting the contrast with an unsupervised model. It does not say that specific target concentrations were training labels; it says separately acquired fluorescence images confirmed the segmentation results, not that they supplied the labels. (LLM, conf=0.87)
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- Authors
- Zhang, J., Zhao, J., Lin, H., Tan, Y., & Cheng, J.-X.
- Affiliations
- Boston University (US)
- Corresponding author
- Cheng, Ji-Xin
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-09-11
- Volume
- 11
- Issue
- 20
- Pages
- 8573-8578
- ISSN
- 1948-7185 (ISSN-L)
- PMC
- PMC9154735
- OpenAlex
- W3086379246
- Cited by
- 54
- References
- 37
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 10 · 2023: 10 · 2022: 12 · 2021: 11
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- The Journal of Physical Chemistry Letters (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (R01CA224275, R33CA223581)
- National Institute of General Medical Sciences (R35GM136223)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Laser-Matter Interactions and Applications
- Keywords
- stimulated Raman scattering, hyperspectral imaging, chemical imaging, DeepChem, DenseNet, label-free imaging, inverse problems, lipid droplets, endoplasmic reticulum, nuclei, cytoplasm, MIA PaCa-2 cells, dithiothreitol, live-cell imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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