AIMC Topic: Optical Imaging

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Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging.

Journal of nanobiotechnology
Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special ...

Accurate intraoperative real-time blood flow assessment of the remnant stomach during robot-assisted distal pancreatectomy with celiac axis resection using indocyanine green fluorescence imaging and da Vinci Firefly technology.

Asian journal of endoscopic surgery
INTRODUCTION: Ischemic gastropathy is one of the unique postoperative complications associated with distal pancreatectomy with celiac axis resection for locally advanced pancreatic cancer. Therefore, it is essential to evaluate blood flow to the stom...

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

Applied and environmental microbiology
In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization,...

LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.

Analytical chemistry
Identifying and quantifying cell death is the basis for all cell death research. Current methods for obtaining these quantitative measurements rely on established biomarkers, yet the marker-based approach suffers from limited marker specificity, high...

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditio...

Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.

Sensors (Basel, Switzerland)
Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixe...

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical canc...

Robot-assisted pericystectomy using Da Vinci Xi surgical system with indocyanine green fluorescence imaging for hepatic cystic echinococcosis.

Asian journal of surgery
OBJECT: The clinical efficacy of robot-assisted laparoscopic pericystectomy using the Da Vinci Xi surgical system plus indocyanine green(ICG) fluorescence imaging and the conventional laparotomy for en bloc pericystectomy was compared.

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

PloS one
Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield...

Challenges and advances in optical 3D mesoscale imaging.

Journal of microscopy
Optical mesoscale imaging is a rapidly developing field that allows the visualisation of larger samples than is possible with standard light microscopy, and fills a gap between cell and organism resolution. It spans from advanced fluorescence imaging...