Products & ReviewLife Sciences

HALO AI

Available: Worldwide

Neural network driven tissue and cell classification for research & routine pathology

Reviews

Average Rating 4.5

|5Scientists have reviewed this product

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Currently the best image viewer and analysis software on the market that I've used.

 

Average Rating 4.3

Application Area:

Image analysis

Currently the best image viewer and analysis software on the market that I've used.

Review Date: 8 Jun 2022

wonderful products, it is far beyond our expectations.

 

Average Rating 5.0

Application Area:

analyze tumor sections

Our lab both have the Halo Image Analysis System and the Halo AI, honestly speaking, the Halo system is beyond our expectations, especially the analysis process, annotation tools, the adjustable parameters, the texture and morphological recognition of nuclear and specific structure, and the after-sale care I strongly recommend you guys to choose it. it really worth the money.

Review Date: 7 Jun 2022

Great results, can't live without this instrument!

 

Average Rating 5.0

Application Area:

Analysis in tissue figure

Scientists can achieve lots of high-quality analysis data about figures by HALO software. Many functions of HALO are very helpful to study images, such as TMA, spatial analysis, high-Plex FL, and especially HALO AI. It is easy to operate. Its interface is simple, friendly, and convenient. Once we have problems, a Halo technician is able to help us to solve them in time.

Review Date: 1 Jun 2022

Great result. Nice tool for translational pathology research.

 

Average Rating 4.3

Application Area:

RNAscope and IHC analysis

We are interested in applying HALO AI in RNAscope and IHC analysis. Thanks to Yongtian ZHAO to give us wonderful training and support. We are improving and confident to apply it in the RNAscope assay development and scoring.

Review Date: 1 Jun 2022

Great

 

Average Rating 4.0

Application Area:

Halo for image research

Quick to use

Review Date: 1 Jun 2022

HALO AI is a collection of train-by-example classification and segmentation tools underpinned by advanced deep learning neural network algorithms. HALO AI classifiers can be trained to quantify tissue classes, to segment tissue classes for analysis with other HALO image analysis modules, to find rare events or cells in tissues, and to categorize cell populations into specific phenotypes.

SIMPLE & INTUITIVE WORKFLOW
HALO AI is fully integrated with the intuitive, easy-to-use HALO and HALO Link viewers and employs a simple three-step workflow. After defining what tissue classes or cell phenotypes you would like to segment, you train the neural network by drawing annotations – no computer programming or AI knowledge required. Trained classifiers can be applied to segment tissue and cells on any whole slide image or region of interest.

POWERFUL TISSUE SEGMENTATION
HALO AI now includes the option of three powerful neural networks – VGG, DenseNet and MiniNet. VGG, a well-known and more traditional network, was used to build the Indica Labs submission in the CAMELYON17 challenge and was the first neural network integrated with HALO AI. DenseNet is a more modern network capable of creating more robust classifiers at higher resolution compared to VGG. MiniNet, a custom network developed at Indica Labs, is more shallow than VGG or DenseNet, but can produce a solution quickly with limited training data and is therefore useful for testing new AI applications.

EXCEPTIONAL CELL CLASSIFICATION
Segment nuclei with the new Segmentation classifier. Utilize HALO AI’s pretrained networks for H&E, single IHC, or DAPI stained images for an out of the box solution. Or train your own nuclei segmentation network for a specific application (unique tissue or advanced staining protocols). Once nuclei are segmented, take it a step further using the Nuclei Phenotyper classifier to automatically assign cells into user defined phenotypes with a few quick training examples.


Application NoteLife Sciences

High impact publications with the HALO AI deep learning classifier add-on

HALO AI, the Deep Learning Classifier Add-on to the HALO® image analysis platform continues to advance research areas as diverse as infectious disease, metabolism, gastric pathology, neuroscience, myology, and oncology and immuno-oncology. In this application note, Indica Labs lists recent HALO AI publications according to research area and highlights which HALO AI network was used.


Application NoteLife Sciences

Depicting the cellular architecture of the tumor microenvironment by integrating hyperplex immunofluorescence and automated image analysis

The tumor microenvironment (TME) is emerging as an important factor that shapes the dynamic of tumor growth, heterogeneity, and response to therapies. In this application eBook, Indica Labs focuses on the phenotyping of cells across different tumor types on a tissue microarray (TMA) with an immuno-oncology panel encompassing 20 biomarkers. They interrogated their TME with the use of the COMET™ automated staining and imaging system, and HALO® and HALO AI image analysis platforms.



Application NoteLife Sciences

RNAscope image analysis using HALO and HALO AI

In this application note, discover how the ISH module and FISH module available with the HALO® image analysis platform can be employed with HALO AI™ to quantitatively assess chromogenic and fluorescence RNAscope assays, respectively.





White PapersLab Automation

High-impact publications with the HALO image analysis platform

Digital pathology with the HALO® image analysis platform continues to advance research areas as diverse as immunology, infectious disease, immuno-oncology, and neuroscience. Download the white paper below to see the caliber of publications that researchers are obtaining, using Indica's cloud-based software.


Application NoteLife Sciences

Robust identification of islets with variable morphology in H&E-stained pancreatic tissue using HALO AITM

In this application note, Indica Labs trains the HALO AITM VGG convolutional neural network (VGG-CNN) to identify islets within pancreatic tissue sections following H&E staining. It demonstrates how it is possible to build a robust classifier to accurately segment islets from surrounding exocrine tissue, irrespective of stain or morphological variability. This study highlights the potential for HALO AI to simplify the pathological evaluation of pancreatic tissue in metabolic research and toxicological pathology.








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