Autor según el artículo: Baulin, Vladimir A.; Usson, Yves; Le Guevel, Xavier;

Departamento: Química Física i Inorgànica

Autor/es de la URV: Baulin, Vladimir

Palabras clave: 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

Resumen: 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.

Áreas temáticas: 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

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: vladimir.baulin@urv.cat

Identificador del autor: 0000-0003-2086-4271

Fecha de alta del registro: 2023-02-19

Volumen de revista: 14

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://onlinelibrary.wiley.com/doi/10.1002/jbio.202100102

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Journal Of Biophotonics. 14 (e202100102):

Referencia 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 del artículo: 10.1002/jbio.202100102

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications