Dataset Information

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

ABSTRACT: Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

SUBMITTER: Pouyet E

PROVIDER: S-EPMC8471921 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Pouyet Emeline E Miteva Tsveta T Rohani Neda N de Viguerie Laurence L

Sensors (Basel, Switzerland) 20210913 18

Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment ap ...[more]

PMID: 34577356

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Project description:While multiplexed fluorescence imaging is frequently used for in vitro microscopy, extending the technique to whole animal imaging in vivo has remained challenging due to the attenuation and scattering of visible and traditional near infrared (NIR-I) wavelengths. Fluorescence imaging using short-wave infrared (SWIR, 1000 - 1700 nm, a.k.a. NIR-II) light enables deeper tissue penetration for preclinical imaging compared to previous methods due to reduced tissue scattering and minimal background autofluorescence in this optical window. Combining NIR-I excitation wavelengths with multiple distinct SWIR emission peaks presents a tremendous opportunity to distinguish multiple fluorophores with high precision for non-invasive, multiplexed anatomical imaging in small animal models. SWIR-emitting semiconductor quantum dots (QDs) with tunable emission peaks and optical stability have emerged as powerful contrast agents, but SWIR imaging demonstrations have yet to move beyond two-color imaging schemes. In this study, we engineered a set of three high quantum yield lead sulfide/cadmium sulfide (PbS/CdS) core/shell QDs with distinct SWIR emissions ranging from 1100 - 1550 nm and utilize these for simultaneous three-color imaging in mice. We first use QDs to non-invasively track lymphatic drainage, highlighting the detailed network of lymphatic vessels with high-resolution with a widefield imaging over a 2 hr period. We then perform multiplexed imaging with all three QDs to distinctly visualize the lymphatic system and spatially overlapping vasculature network. This work establishes optimized SWIR QDs for next-generation multiplexed preclinical imaging, moving beyond the capability of previous dual-labeling techniques. The capacity to discriminate several fluorescent labels through non-invasive NIR-I excitation and SWIR detection unlocks numerous opportunities for studies of disease progression, drug biodistribution, and cell trafficking dynamics in living organisms.

Dataset Information

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Publications

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Similar Datasets

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