AIMC Topic: Microscopy, Fluorescence

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Deep learning for in vivo near-infrared imaging.

Proceedings of the National Academy of Sciences of the United States of America
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 ...

Deep-learning method for data association in particle tracking.

Bioinformatics (Oxford, England)
MOTIVATION: Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typicall...

3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Optics express
Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and...

OrgaNet: A Robust Network for Subcellular Organelles Classification in Fluorescence Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic identification of subcellular compartments of proteins in fluorescence microscopy images is an important task to quantitatively evaluate cellular processes. A common problem for the development of deep learning based classifiers is that the...

Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Segmentation of cell nuclei in fluorescence microscopy images provides valuable information about the shape and size of the nuclei, its chromatin texture and DNA content. It has many applications such as cell tracking, counting and classification. In...

Computational cannula microscopy of neurons using neural networks.

Optics letters
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more ...

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Optics letters
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, const...

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning.

Neuroinformatics
The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screenin...

A Cell Segmentation/Tracking Tool Based on Machine Learning.

Methods in molecular biology (Clifton, N.J.)
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (se...