AIMC Topic: Microscopy, Confocal

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Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

Journal of biomedical optics
SIGNIFICANCE: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessita...

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Microscopy research and technique
Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography a...

Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

The ocular surface
PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal micr...

A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.

The ocular surface
PURPOSE: To develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3).

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Survey of ophthalmology
Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Co...

A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging.

Cell calcium
The release of Ca ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classi...

Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet's membrane keratoplasty (DMEK).

Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images.

Scientific reports
Infectious keratitis refers to a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. Among these disorders, fungal keratitis (FK) and acanthamoeba keratitis (AK) are particularly severe ...

NerveStitcher: Corneal confocal microscope images stitching with neural networks.

Computers in biology and medicine
Corneal nerves are of great interest to clinicians and scientists due to their potential for the diagnosis of early neurological disorders. In vivo confocal microscopy (IVCM) has been used as a novel and reliable tool for observing and quantifying co...