Deep learning: step forward to high‐resolution in vivo shortwave infrared imaging
Open accessAbstract
Shortwave infrared window (SWIR: 1000–1700 nm) represents a major improvement compared to the NIR‐I region (700–900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods such as X‐ray and opto‐acoustic imaging. Main obstacles in SWIR imaging are the noise and scattering from tissues and skin that reduce the precision of the method. We demonstrate that the combination of SWIR in vivo imaging in the NIR‐IIb region (1500–1700 nm) with advanced deep learning image analysis allows to overcome these obstacles and making a large step forward to high resolution imaging: it allows to precisely segment vessels from tissues and noise, provides morphological structure of the vessels network, with learned pseudo‐3D shape, their relative position, dynamic information of blood vascularization in depth in small animals and distinguish the vessels types: artieries and veins. For demonstration we use neural network IterNet that exploits structural redundancy of the blood vessels, which provides a useful analysis tool for raw SWIR images.
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Image analysis using deep neural networks can partially address the limited spatial resolution by transforming a blurred image to a much higher-clarity image closely resembling the ground truth.
“This can be partially addressed by applying an image analysis using deep neural networks to transform a blurred image to a much higher-clarity image closely resembling the ground truth.198,199”
From The Pursuit of Shortwave Infrared-Emitting Nanoparticles with Bright Fluorescence through Molecular Design and Excited-State Engineering of Molecular Aggregates (2022) · cites this paper as [199]
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- Authors
- Baulin, V. A., Usson, Y., & Le Guével, X.
- Affiliations
- Universitat Rovira i Virgili (ES)
- Translational Innovation in Medicine and Complexity (FR)
- Cancer Targets and Experimental Therapeutics, Cancer Targets and Experimental Therapeutics University of Grenoble Alpes Grenoble France
- Corresponding author
- Baulin, Vladimir A.
- Venue
- Journal of Biophotonics
- Publisher
- Wiley
- Published
- 2021-05-12
- Volume
- 14
- Issue
- 7
- Pages
- e202100102-e202100102
- ISSN
- 1864-063X (ISSN-L)
- 1864-0648
- OpenAlex
- W3158432244
- Cited by
- 10
- References
- 26
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Biophotonics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche
- Plan National Cancer
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Photoacoustic and Ultrasonic Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Keywords
- short-wave infrared imaging, in vivo imaging, deep learning, vessel segmentation, small animal imaging, vascularization
- MeSH terms
- Deep Learning, Animals, Infrared Rays, Radio Waves, Neural Networks, Computer
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