The Power of AI in the Hands of the Pathologists

Deep Learning Classifier Add-On

Neural Network Driven Tissue and Cell Classification for Research & Routine Pathology

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. Skip down to see some applications tested with our built-in networks.

Exceptional Cell Classification

Segment nuclei with the nuclei 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. Keep scrolling to see some applications tested with our Nuclei Segmentor and Nuclei Phenotyper.

HALO AI Collaboration

Platform Compatibility

HALO AI is compatible with all of the file formats that can be used in HALO and HALO Link. Not on the list? Email us your requirements.

File Formats:

Non-proprietary (JPG, TIF, OME.TIFF)
Nikon (ND2)
3DHistech (MRXS)
Akoya (QPTIFF, component TIFF)
Olympus / Evident (VSI)
Hamamatsu (NDPI, NDPIS)
Aperio (SVS, AFI)
Zeiss (CZI)
Leica (SCN, LIF)
Ventana (BIF)
Philips (iSyntax, i2Syntax)
KFBIO (KFB, KFBF)
DICOM (DCM*)
*whole-slide images

Free Image Analysis

See HALO AI in action on up to three of your images with a free analysis.

What Our Customers Have to Say

Read independent SelectScience reviews on our HALO AI Deep Learning Classifier Add-on and learn how these customers are using Indica Labs’ solutions to streamline their workflows.

Knowledge Center

Learn more about HALO AI image analysis by exploring the tabs below.

Characterizing the Cellular Architecture of the Tumor Microenvironment Using Imaging Mass Cytometry and Digital Image Analysis with the HALO® and HALO AI Platform

See in this app note how automated analysis of highly multiplexed IMC images using HALO and HALO AI yields rich cellular and spatial data from a streamlined workflow.

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Glomeruli Application Note

Don’t let varied stain selections hold back your digital image analysis of kidney sections! With HALO AI you can develop a tissue classifier that accurately detects and segments glomeruli in sections across a range of stains including H&E, trichrome, PAMS, and DAB with hematoxylin.

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Camelyon 17 Application Note

CAMELYON17 challenged teams to develop automated methods for identifying and staging breast cancer metastases; read how the performance of a HALO AI classifier stood out among commercial entries!

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HALO and HALO Link NCI Enterprise Cloud Deployment

After the National Cancer Institute migrated their HALO, HALO AI, and HALO Link deployments to the cloud, they experienced 50% faster HALO image analysis in the cloud environment! Learn about additional benefits of cloud deployment for the NCI, considerations they made when planning their deployment, and more in this case study.

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Sequential Same Slide Multiplex IF and H&E Staining for Combined Phenotypic and Morphological Characterization of FFPE Tissue Sections

Reveal more data on a single slide with this workflow for sequential same slide fluorescence and H&E staining and imaging using HALO and HALO AI.

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Qualitative and Quantitative Evaluation of the Tissue Micro-Environment by High-Resolution 17-Plex Immunofluorescence Reveals Distinct Populations

Check out this eBook to see how AI-based nuclear segmentation and tissue classification can streamline cell phenotyping across tissue compartments and samples.

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Our customers are making vital discoveries in oncology, neuroscience, and diabetes research. Check out some of the research our customers are publishing using the HALO AI Deep Learning Classifier Add-on.

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

Predictive biomarkers to determine which non small cell lung cancer (NSCLC) patients will respond to immunotherapy are currently lacking. In this Nature Cancer publication, the authors combine computed tomography imaging, PD-L1 immunohistochemistry (IHC), and genomic data to predict responses to...

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Prognostic Value of CD8+ Lymphocytes in Hepatocellular Carcinoma and Perineoplastic Parenchyma Assessed by Interface Density Profiles in Liver Resection Samples

This study examined CD8+ cell distribution in hepatocellular carcinoma (HCC) and peritumoral liver tissue to investigate the overall survival (OS) and recurrence-free survival (RFS). It is well understood that CD8+ lymphocytes are involved in both the anti-tumor response and in...

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Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors

EZH2 is the catalytic component of Polycomb Repressive Complex 2 (PRC2) and performs trimethylation of histone H3 at lysine 27 (H3K27me3) to silence chromatin. PRC2 is known to play different roles in different cancers and inhibitors of PRC2 histone methyltransferase...

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HALO AI High Impact Publications

Review this selection of high-impact publications for examples of HALO AI applications in fields ranging from metabolism to immuno-oncology and myology.

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HALO AI White Paper

Check out this white paper for an in-depth introduction to the deep learning-based add-on to HALO, including available networks, common functions, and the “train – validate – test” Validation workflow.

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Intro to the HALO AI Toolbox

Learn about each of the HALO AI networks and their applications

Neurobiology Image Analysis with HALO® and HALO AI

Learn how to maximize utility of the new Microglial and Object Colocalization modules and how HALO AI can advance neurobiology image analysis.

Indica Labs Launches Comprehensive Management Service for HALO® Software in AWS Cloud-Hosted Environments

Indica Labs offers AWS-hosted deployments of life science HALO software, including HALO, HALO AI, and HALO Link, as well as deployments of the clinical HALO AP platform

Literature

Learn more about applications of HALO AI in this selection of our most popular application notes, publication lists, white papers, and more!

Camelyon 17 Application Note

CAMELYON17 challenged teams to develop automated methods for identifying and staging breast cancer metastases; read how the performance of a HALO AI classifier stood out among commercial entries!

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