Microscopy

How we are enhancing microscopy imaging solutions with AI and automated image analysis:

Whether you’re enhancing on-device software, or building up in-house image analysis capabilities, RSIP Vision can help accelerate your development. Our field-tested technologies will help you automate the most challenging tasks with state-of-the art accuracy and robustness.

AI and Computer Vision Technologies

Our powerful detection and segmentation technology to support analysis of clumped and overlapping nuclei is robust to multiple scales of illumination, and can detect tumor regions at multiple image scales. We have strong experience in all microscopy imaging modalities, providing customized solutions for a wide range of image analysis tasks including cell classification, robust signal measurements, and more.

Case study

Cell Segmentation & Abnormality Risk Estimation

The Challenge:

A manufacturer of leading FISH imaging platforms required robust nuclei segmentation and abnormality risk estimation modules for their integrated software
The focus was tightly clumped and overlapping cells
Precise interpretation is necessary to overcome ambiguity, avoid false positives, and detect all rare events

Our Approach:

We created customized state-of-the-art segmentation technology, based on deep learning, for client dataset image characteristics
We created robust segmentation & marker assignment algorithms for overlapping cells
We implemented custom abnormality risk measures

Results:

Nuclei detection: 98% precision, recall, and F1 score
Nuclei segmentation: 0.98 mean Dice score
Key marker detection: 95% precision, recall, and F1 score

Learn more

Learn more solutions and technologies from this field

Multiplex IF Analysis

The use of deep learning for analysis of multiplex IF has allowed for a much greater accuracy level for the correct phenotypic classification of cells. When combined with RSIP Vision‘s advanced nuclear detection capability, it allows for the simultaneous analysis of multiple florescent markers on a cell by cell basis. This tool is well suited for multiple applications, especially when using multiple markers to characterize distinct cell populations such as in immune-oncology and IBD.

Circulating Tumor Cells (CTCs)

Circulating tumor cells (CTCs) are rare cancer cells that originate from a tumor and then travel through the patient’s blood or lymphatic system. CTCs have a high risk of metastasizing elsewhere in the body, which can be very dangerous. It is well-known that finding and analyzing these rare cells can give a very good basis for the right prognosis and the most suitable treatment for the patient. The problem with CTCs is that it is very difficult to find them among thousands of regular cells. This makes testing for them

Tissue Analysis with AI

New AI technologies by RSIP Vision are very powerful in analysis of tissues and histopathology. This complex task, which has been haunting for years the medical community, has now a very practical solution: deep learning gives very fruitful results to several challenges, like the segmentation of cells and nucleus and the classification of the cells according to the detected pathologies.

Detection and Segmentation of Dendritic Cells

Dendritic cells are a type of antigen-presenting cells and have an integral part in the normal functioning immune system, in that they help to initiate primary immune response. Dendritic cells are typically present in tissues that come in contact with the external environment. That includes the skin, the nasal cavity lining, the lungs and parts of the digestive tract. Segmentation of Dendritic Cells with Deep Learning RSIP vision has developed a set of artificial intelligence tools to find, detect and track dendritic cells. Due to dendritic cells being an integral

Classification and Segmentation of Dendritic Cells

Dry eye disease (DED) is one of the most common ophthalmic disorders. Inflammation of the ocular surface is controlled by corneal antigen-presenting cells called dendritic cells (DCs), which induce T-cell activation, and play a critical role in the pathogenesis of dry eye disease. The density of corneal DC is correlated with both symptoms and clinical signs in dry eye, making it an attractive non-invasive and responsive surrogate biomarker to assess the severity of corneal inflammation. To evaluate the effect of inflammation on the cornea structures and function, coronal images at

Cell Classification - pattern recognition classification techniques

Biomedical image processing and algorithms

Biomedical image processing (or more precisely, biomedical image and signal processing) consists in sophisticated analytical methods and algorithms, ubiquitously found integrated into diagnostic equipment and clinical imaging modalities. The contribution of biomedical image processing and computer vision algorithms has signaled a paradigm shift in clinical practices and care in several ways: first, by providing accurate prognosis; second, by reducing the amount of expensive and invasive examinations, which implies sparing patient risks and reducing treatment costs, while at the same time increasing accuracy. The need for new practices and use of

Microscopy

How we are enhancing microscopy imaging solutions with AI and automated image analysis:

Whether you’re enhancing on-device software, or building up in-house image analysis capabilities, RSIP Vision can help accelerate your development. Our field-tested technologies will help you automate the most challenging tasks with state-of-the art accuracy and robustness.

AI and Computer Vision Technologies

Nuclei Segmentation

Tumor Region Detection

Cell Classification

Preprocessing

Signal Detection

Custom Solutions

H&E

Multiplex Immunofluorescence

FISH & Liquid Biopsy

DAB + EOS MBP

Confocal Microscopy

Karyotype

MOHS

Structured Illumination Microscopy (SIM)

Electron Microscopy

Case study

Cell Segmentation & Abnormality Risk Estimation

The Challenge:

Our Approach:

Results:

Learn more

Recent News

Upcoming Events

Subscribe to Our Magazines

Follow us