Hyperspectral imaging for thermal effect monitoring in in vivo liver during laser ablation

2019 ◽  
Author(s):  
M. De Landro ◽  
P. Saccomandi ◽  
M. Barberio ◽  
E. Schena ◽  
M. J. Marescaux ◽  
...  
Keyword(s):  
Laser Ablation ◽  
Thermal Effect ◽  
Hyperspectral Imaging ◽  
Effect Monitoring

scholarly journals Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3827
Author(s):  
Gemma Urbanos ◽  
Alberto Martín ◽  
Guillermo Vázquez ◽  
Marta Villanueva ◽  
Manuel Villa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessel ◽  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Venous Blood ◽  
Healthy Tissue ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

scholarly journals Myelin replacement triggered by single-cell demyelination in mouse cortex

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Snaidero ◽  
Martina Schifferer ◽  
Aleksandra Mezydlo ◽  
Bernard Zalc ◽  
Martin Kerschensteiner ◽  
...  
Keyword(s):  
Laser Ablation ◽  
Single Cell ◽  
In Vivo Imaging ◽  
Active Participant ◽  
Cell Replacement ◽  
Myelin Sheaths ◽  
Unmyelinated Axons ◽  
Single Axon ◽  
Mouse Cortex

Abstract Myelin, rather than being a static insulator of axons, is emerging as an active participant in circuit plasticity. This requires precise regulation of oligodendrocyte numbers and myelination patterns. Here, by devising a laser ablation approach of single oligodendrocytes, followed by in vivo imaging and correlated ultrastructural reconstructions, we report that in mouse cortex demyelination as subtle as the loss of a single oligodendrocyte can trigger robust cell replacement and remyelination timed by myelin breakdown. This results in reliable reestablishment of the original myelin pattern along continuously myelinated axons, while in parallel, patchy isolated internodes emerge on previously unmyelinated axons. Therefore, in mammalian cortex, internodes along partially myelinated cortical axons are typically not reestablished, suggesting that the cues that guide patchy myelination are not preserved through cycles of de- and remyelination. In contrast, myelin sheaths forming continuous patterns show remarkable homeostatic resilience and remyelinate with single axon precision.

scholarly journals A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis

2019 ◽  
Vol 158 ◽  
pp. 258-270 ◽  
Author(s):  
Jun-Li Xu ◽  
Alexia Gobrecht ◽  
Daphné Héran ◽  
Nathalie Gorretta ◽  
Marie Coque ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Leaf Analysis ◽  
In Vivo Detection

scholarly journals Self-Assembled Monolayers of Copper Sulfide Nanoparticles on Glass as Antibacterial Coatings

Nanomaterials ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 352 ◽  
Author(s):  
Chiara Gargioni ◽  
Mykola Borzenkov ◽  
Laura D’Alfonso ◽  
Paola Sperandeo ◽  
Alessandra Polissi ◽  
...  
Keyword(s):  
Thermal Effect ◽  
Bacterial Adhesion ◽  
Copper Sulfide ◽  
Near Infrared ◽  
Thermal Action ◽  
Self Assembled Monolayers ◽  
Nir Light ◽  
Copper Release ◽  
Assembled Monolayers

We developed an easy and reproducible synthetic method to graft a monolayer of copper sulfide nanoparticles (CuS NP) on glass and exploited their particular antibacterial features. Samples were fully characterized showing a good stability, a neat photo-thermal effect when irradiated in the Near InfraRed (NIR) region (in the so called “biological window”), and the ability to release controlled quantities of copper in water. The desired antibacterial activity is thus based on two different mechanisms: (i) slow and sustained copper release from CuS NP-glass samples, (ii) local temperature increase caused by a photo-thermal effect under NIR laser irradiation of CuS NP–glass samples. This behavior allows promising in vivo applications to be foreseen, ensuring a “static” antibacterial protection tailored to fight bacterial adhesion in the critical timescale of possible infection and biofilm formation. This can be reinforced, when needed, by a photo-thermal action switchable on demand by an NIR light.

scholarly journals Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer’s disease

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Hadoux ◽  
Flora Hui ◽  
Jeremiah K. H. Lim ◽  
Colin L. Masters ◽  
Alice Pébay ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hyperspectral Imaging ◽  
Spectral Difference ◽  
Non Invasive ◽  
Potential Biomarker ◽  
Negative Controls ◽  
The Brain ◽  
Independent Cohort

Abstract Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment (n = 15), and age-matched PET-negative controls (n = 20). Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load.

scholarly journals A Percutaneous Catheter for In Vivo Hyperspectral Imaging of Cardiac Tissue: Challenges, Solutions and Future Directions

2020 ◽  
Vol 11 (5) ◽  
pp. 560-575 ◽  
Author(s):  
Kenneth Armstrong ◽  
Cinnamon Larson ◽  
Huda Asfour ◽  
Terry Ransbury ◽  
Narine Sarvazyan
Keyword(s):  
Hyperspectral Imaging ◽  
Cardiac Tissue ◽  
Future Directions ◽  
Percutaneous Catheter

scholarly journals Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5481 ◽  
Author(s):  
Beatriz Martinez ◽  
Raquel Leon ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Juan F. Piñeiro ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Brain Cancer ◽  
Near Infrared ◽  
Relevant Information ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Spectral Bands ◽  
Tumor Identification ◽  
Spectral Ranges

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.

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