AIMC Topic: Microscopy, Confocal

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Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.

Nature methods
We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity ...

[Digital Image Processing and Deep Neural Networks in Ophthalmology - Current Trends].

Klinische Monatsblatter fur Augenheilkunde
The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodologica...

Toward Automated Bladder Tumor Stratification Using Confocal Laser Endomicroscopy.

Journal of endourology
Urothelial carcinoma of the bladder (UCB) is the most common urinary cancer. White-light cystoscopy (WLC) forms the corner stone for the diagnosis of UCB. However, histopathological assessment is required for adjuvant treatment selection. Probe-base...

Intra- and Interspecies Variability of Single-Cell Innate Fluorescence Signature of Microbial Cell.

Applied and environmental microbiology
Here we analyzed the innate fluorescence signature of the single microbial cell, within both clonal and mixed populations of microorganisms. We found that even very similarly shaped cells differ noticeably in their autofluorescence features and that ...

Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy i...

Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.

International journal of computer assisted radiology and surgery
PURPOSE: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been propos...

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Medical image analysis
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example...

Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages.

IEEE/ACM transactions on computational biology and bioinformatics
Segmenting bioimage based filaments is a critical step in a wide range of applications, including neuron reconstruction and blood vessel tracing. To achieve an acceptable segmentation performance, most of the existing methods need to annotate amounts...

Photoluminescence-tunable fluorescent carbon dots-deposited silver nanoparticle for detection and killing of bacteria.

Materials science & engineering. C, Materials for biological applications
Innovative methods to detect and kill pathogenic bacteria have a pivotal role in the eradication of infectious diseases and the prevention of the growth of antibiotic-resistant bacteria. The combination of fluorescent carbon dots (FCDs) with silver n...

Deep learning enables cross-modality super-resolution in fluorescence microscopy.

Nature methods
We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on ...