Autor segons l'article: Baulin, Vladimir A.; Usson, Yves; Le Guevel, Xavier;
Departament: Química Física i Inorgànica
Autor/s de la URV: Baulin, Vladimir
Paraules clau: Tissue Thermography (imaging) Temporal and spatial Swir Structural redundancy Shortwave infrared imaging Short wave infrared Relative positions Radiofrequency radiation Radio waves Neural networks, computer Morphological structures Microvessels Infrared rays Infrared radiation Infrared devices Images Image segmentation Image analysis Histology High-resolution imaging Fluorescence Dynamic information Deep learning Blood vessels Blood Animals Animal swir microvessels fluorescence
Resum: 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.
Àrees temàtiques: Química Physics and astronomy (miscellaneous) Physics and astronomy (all) Optics Odontología Medicina veterinaria Medicina iii Medicina ii Medicina i Materials science (miscellaneous) Materials science (all) Materiais Interdisciplinar General physics and astronomy General materials science General engineering General chemistry General biochemistry,genetics and molecular biology Farmacia Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias ii Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Chemistry (miscellaneous) Chemistry (all) Biotecnología Biophysics Biochemistry, genetics and molecular biology (miscellaneous) Biochemistry, genetics and molecular biology (all) Biochemical research methods Astronomia / física
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: vladimir.baulin@urv.cat
Identificador de l'autor: 0000-0003-2086-4271
Data d'alta del registre: 2024-07-27
Volum de revista: 14
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://onlinelibrary.wiley.com/doi/10.1002/jbio.202100102
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Journal Of Biophotonics. 14 (e202100102):
Referència de l'ítem segons les normes APA: Baulin, Vladimir A.; Usson, Yves; Le Guevel, Xavier; (2021). Deep learning: step forward to high-resolution in vivo shortwave infrared imaging. Journal Of Biophotonics, 14(e202100102), -. DOI: 10.1002/jbio.202100102
DOI de l'article: 10.1002/jbio.202100102
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications