ArticleIEEE transactions on medical imaging2020
GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 6 citations in OpenAlex.
- C3VDv2 - Colonoscopy 3D video dataset with enhanced realism.Scientific data · 2026Article
- Simulation-Driven Spatial Frequency Domain Imaging and Deep Learning for Subsurface Fruit Bruise Discrimination.Foods (Basel, Switzerland) · 2026Article
- Structured light radial-view endoscope PROBE for IMAGING of colorectal tissue.Optics letters · 2025Article
- Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.Journal of biomedical optics · 2025Article
- Speckle-illumination spatial frequency domain imaging with a stereo laparoscope for profile-corrected optical property mapping.Journal of biomedical optics · 2025Article
- A Best-Fitting B-Spline Neural Network Approach to the Prediction of Advection-Diffusion Physical Fields with Absorption and Source Terms.Entropy (Basel, Switzerland) · 2024Article
- Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.Journal of biomedical optics · 2024Article
- Colonoscopy 3D video dataset with paired depth from 2D-3D registration.Medical image analysis · 2023Article
- Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software.Biomedical optics express · 2023Article
- Unrolled-DOT: an interpretable deep network for diffuse optical tomography.Journal of biomedical optics · 2023Article
- Training generative adversarial networks for optical property mapping using synthetic image data.Biomedical optics express · 2022Article
- Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections.Biophysical reviews · 2022Review
- Phase function estimation from a diffuse optical image via deep learning.Physics in medicine and biology · 2022Article
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- Spatial frequency domain imaging technology based on Fourier single-pixel imaging.Journal of biomedical optics · 2022Article
- Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures.Scientific reports · 2021Article
- Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall.Journal of biomedical optics · 2021Article
- Machine learning for real-time optical property recovery in interstitial photodynamic therapy: a stimulation-based study.Biomedical optics express · 2021Article
- Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging.Journal of biomedical optics · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties at 660 nm from in vivo human hands and feet, freshly resected human esophagectomy samples, and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP with a single structured-light input image estimates the reduced scattering and absorption coefficients with 60% higher accuracy than SSOP while GANPOP with a single flat-field illumination image achieves similar accuracy to SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on structured-illumination images of human specimens and phantoms estimates optical properties with approximately 46% improvement over SSOP, indicating adaptability to new, unseen tissue types. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.