scholarly journals Deep learning image analysis for in vivo shortwave infrared imaging

2021 ◽  
Author(s):  
Vladimir A. Baulin ◽  
Yves Usson ◽  
Xavier Le Guével
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Infrared Imaging ◽  
Morphological Structure ◽  
Analysis Tool ◽  
Large Step ◽  
Cheap Alternative ◽  
Resolution Imaging ◽  
Shortwave Infrared

AbstractIn vivo optical imaging is a fast growing field that offers great perspectives for biomedical applications. In particular, imaging in the shortwave infrared window (SWIR: 1000-1700 nm) represents 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 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 the network IterNet that exploits structural redundancy of the blood vessels (L. Li, et.al., The IEEE WACV, 2020), which provides a useful analysis tool for raw SWIR images.

scholarly journals Shortwave Infrared Imaging with J-Aggregates Stabilized in Hollow Mesoporous Silica Nanoparticles

2018 ◽  
Author(s):  
Wei Chen ◽  
ChiAn Cheng ◽  
Emily Cosco ◽  
Shyam Ramakrishnan ◽  
Jakob Lingg ◽  
...  
Keyword(s):  
Mesoporous Silica ◽  
Contrast Agents ◽  
Silica Nanoparticles ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Mesoporous Silica Nanoparticles ◽  
J Aggregates ◽  
Hollow Mesoporous Silica ◽  
Shortwave Infrared

Tissue is translucent to shortwave infrared (SWIR) light, rendering optical imaging superior in this region. However, the widespread use of optical SWIR imaging has been limited, in part, by the lack of bright, biocompatible contrast agents that absorb and emit light above 1000 nm. J-aggregation offers a means to transform stable, near-infrared (NIR) fluorophores into red-shifted SWIR contrast agents. Here we demonstrate that hollow mesoporous silica nanoparticles (HMSNs) can template the J-aggregation of NIR fluorophore IR-140 to result in nanomaterials that absorb and emit SWIR light. The J-aggregates inside PEGylated HMSNs are stable for multiple weeks in buffer and enable high resolution imaging <i>in vivo</i>with 980 nm excitation.

scholarly journals Surface chemistry-mediated metal nanoclusters for in vivo shortwave infrared imaging

2021 ◽  
Author(s):  
Xavier Le Guevel ◽  
Benjamin Musnier ◽  
Karl D. Wegner ◽  
Maxime Henry ◽  
Agnes Desroches-Castan ◽  
...  
Keyword(s):  
Surface Chemistry ◽  
Infrared Imaging ◽  
Metal Nanoclusters ◽  
Shortwave Infrared

scholarly journals Deep learning for in vivo near-infrared imaging

2020 ◽  
Vol 118 (1) ◽  
pp. e2021446118
Author(s):  
Zhuoran Ma ◽  
Feifei Wang ◽  
Weizhi Wang ◽  
Yeteng Zhong ◽  
Hongjie Dai
Keyword(s):  
Deep Learning ◽  
Light Scattering ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Imaging Modality ◽  
Molecular Probes ◽  
Clinical Diagnostics ◽  
Light Sheet Microscopy ◽  
Lymph Node Imaging

Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500–1,700 nm) is the most effective one-photon approach to suppressing light scattering and maximizing imaging penetration depth, but relies on nanoparticle probes such as PbS/CdS containing toxic elements. On the other hand, imaging the NIR-I (700–1,000 nm) or NIR-IIa window (1,000–1,300 nm) can be done using biocompatible small-molecule fluorescent probes including US Food and Drug Administration-approved dyes such as indocyanine green (ICG), but has a caveat of suboptimal imaging quality due to light scattering. It is highly desired to achieve the performance of NIR-IIb imaging using molecular probes approved for human use. Here, we trained artificial neural networks to transform a fluorescence image in the shorter-wavelength NIR window of 900–1,300 nm (NIR-I/IIa) to an image resembling an NIR-IIb image. With deep-learning translation, in vivo lymph node imaging with ICG achieved an unprecedented signal-to-background ratio of >100. Using preclinical fluorophores such as IRDye-800, translation of ∼900-nm NIR molecular imaging of PD-L1 or EGFR greatly enhanced tumor-to-normal tissue ratio up to ∼20 from ∼5 and improved tumor margin localization. Further, deep learning greatly improved in vivo noninvasive NIR-II light-sheet microscopy (LSM) in resolution and signal/background. NIR imaging equipped with deep learning could facilitate basic biomedical research and empower clinical diagnostics and imaging-guided surgery in the clinic.

scholarly journals Deep learning method for comet segmentation and comet assay image analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yiyu Hong ◽  
Hyo-Jeong Han ◽  
Hannah Lee ◽  
Donghwan Lee ◽  
Junsu Ko ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Comet Assay ◽  
Tail Length ◽  
High Sensitivity ◽  
Analysis Tool ◽  
Test Results ◽  
Practical Application ◽  
Average Precision ◽  
The Arts

Abstract Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

scholarly journals Shortwave Infrared Imaging with J-Aggregates Stabilized in Hollow Mesoporous Silica Nanoparticles

2018 ◽  
Author(s):  
Wei Chen ◽  
ChiAn Cheng ◽  
Emily Cosco ◽  
Shyam Ramakrishnan ◽  
Jakob Lingg ◽  
...  
Keyword(s):  
Mesoporous Silica ◽  
Contrast Agents ◽  
Silica Nanoparticles ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Mesoporous Silica Nanoparticles ◽  
J Aggregates ◽  
Hollow Mesoporous Silica ◽  
Shortwave Infrared

Tissue is translucent to shortwave infrared (SWIR) light, rendering optical imaging superior in this region. However, the widespread use of optical SWIR imaging has been limited, in part, by the lack of bright, biocompatible contrast agents that absorb and emit light above 1000 nm. J-aggregation offers a means to transform stable, near-infrared (NIR) fluorophores into red-shifted SWIR contrast agents. Here we demonstrate that hollow mesoporous silica nanoparticles (HMSNs) can template the J-aggregation of NIR fluorophore IR-140 to result in nanomaterials that absorb and emit SWIR light. The J-aggregates inside PEGylated HMSNs are stable for multiple weeks in buffer and enable high resolution imaging <i>in vivo</i>with 980 nm excitation.

KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

2019 ◽  
pp. 535-551 ◽  
Author(s):  
Dinesh Pothineni ◽  
Martin R. Oswald ◽  
Jan Poland ◽  
Marc Pollefeys
Keyword(s):  
Image Analysis ◽  
Deep Learning
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