Advancing Discovery with Trainable AI
Insightful and robust AI creation for pathology
Unlock Limitless Possibilities with HALO AI
HALO AI has you covered. Whether you’re looking to solve image analysis challenges in segmentation, classification, or phenotyping, the advanced deep learning networks of HALO AI are easily tuned for your specific application with the train-by-example interface.
Diverse and Powerful AI
Underpinned by modern deep learning networks, HALO AI provides a collection of segmentation, classification, and phenotyping tools for brightfield and fluorescence applications. Train a classifier to quantify tissue classes, to find rare events or cells in tissues, or to categorize cell populations into specific phenotypes.
AI-Powered Annotation
Use a point and click workflow to rapidly annotate images and develop training data.
Nuclear Segmentation
Choose between several trainable AI-based nuclear segmentation networks to optimize segmentation accuracy when nuclear morphologies vary.
Membrane Segmentation
Train AI-powered membrane segmentation networks to accurately delineate cell membranes for your specific assay.
Tissue Segmentation
Train AI-based MiniNet and DenseNet networks to classify tissue into user-defined classes.
Cell & Object Phenotyping
Quickly train a phenotyper to quantify cell types or objects of interest.
Slide Quality Control
Leverage a trainable AI-powered quality control network to detect common artifacts in H&E and IHC images.
Simple & Intuitive Workflow
Define Classes
Train Network
Apply Classifier
Choose to integrate your trained HALO AI networks in multiple locations within HALO modules to perform functions including tissue classification, nuclear and membrane segmentation, and phenotyping. Alternatively, apply HALO AI directly to an entire study of whole slide images or to select regions of interest – the choice is yours.
Manage Variability
Extreme variability runs rife in image analysis but is no match for HALO AI. Common sources of variability include diverse morphologies, alterations of morphologies from staining protocols, differences in tissue quality, uneven staining, and more.
HALO AI can be easily trained to accommodate variability to deliver accurate segmentation and classification results across large studies. HALO AI can even be trained to work across vastly different stains such as PAMS, Trichrome, H&E, and IHC.
Exceptional Identification
Pre-trained nuclear and membrane segmentors available in HALO are advanced tools for nuclear and membrane segmentation, but when you need to optimize a network for a bespoke application, you need HALO AI.
As with the tissue classification and segmentation networks, you can quickly add training data with the AI-based annotation tool and train the network to optimally segment nuclei or cell membranes. And once trained, any HALO AI network can be incorporated into HALO modules for maximum utility.
Powerful Tissue Classification
The degree of variability encountered in pathology often demands an approach above and beyond a simple random forest tissue classifier.
HALO AI has two advanced pre-trained networks capable of creating high-resolution classifiers in brightfield and fluorescence, depending on how much training data and time are available for training and optimization.
Curious to see HALO AI in action on your images?
Advanced Phenotyping
Cell phenotyping can be used on any of the myriad supported image types to automatically assign cells into user defined phenotypes. Simply select a nuclear or membrane segmentation network, provide a few quick training examples, and train the network to identify phenotypes of interest.
Phenotyping is a highly effective and flexible tool to quantify cells based on morphology when biomarker information is absent and can assist with quantification and characterization of cells with complex morphologies, such as neuronal cells.
Top Notch Training and Support
At Indica Labs, we take pride in providing top-quality support. Unlimited IT support, scientific support, training, and access to our Learning Portal are included with every license at no additional cost. With support staff in the US, UK, EU, China, and Japan, you are supported in any time zone.
Wondering how easy developing your own algorithm can be without programming experience? Reach out to us to find out how intuitive HALO AI’s train-by-example interface really is.
What Our Customers Have to Say
Read independent reviews on our HALO AI Deep Learning Classifier Add-on and learn how these customers are using Indica Labs’ solutions to streamline their workflows.
Platform Compatibility
HALO AI is compatible with all of the file formats that can be used in HALO and HALO Link. Yours 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
Advancing Discovery with AI: Features of HALO AI
Quickly acquire training annotations with the new AI annotation tool in HALO AI. Simply point and click your object of interest to add an annotation.
Use real-time tuning in HALO AI to watch a network as it trains in real time. Toggle the mark up on and off to evaluate performance, choose to add training data, or change parameters on-the-fly.
Once a HALO AI model is trained, a probability map can be used as an alternative output to a traditional mask to evaluate performance. Use real-time tuning to select an appropriate probability cut-off for a given class and view the output in a heatmap where blue represents the lowest probability and red the highest.
Investigate results with interactive markup images where you can toggle on and off each population of interest. Interactive markups can be combined with probability thresholding and are especially valuable in exploring validation outputs.
Deploy your HALO AI models in the context of HALO modules for maximum image analysis utility. Choose to utilize one or more models such as nuclear segmentation, membrane segmentation, tissue classification, cell or object phenotyping, and/or SlideQC.
Collaborate on the development of AI using the HALO Link collaborative image management platform. Simply invite your colleagues to a study and have them add training data.
Create classifier pipelines by connecting multiple HALO AI classifiers into a single workflow. Choose to run within HALO AI or within a HALO module.
Model interoperability leveraging an open format ONNX allows you to move networks between machines, bring in outside ONNX models, or export your model in the ONNX format thanks to the flexible import and export options in HALO AI.
Leverage the three-part Train – Validate – Test workflow to address the question: How good is my HALO AI classifier? Quantitative metrics are output based on the type of network.
Speed up your HALO AI analysis by adapting your trained network for your specific GPU. Also, you can queue training jobs based on number of iterations or time and set it to run at the end of your workday to maximize productivity and evaluate multiple model variants.
Discover HALO AI Apps
Explore our most popular HALO AI apps. You can filter by category and use the arrows at the bottom of the list to view additional options.
The Breast IHC Tumor Detection App is a pre-trained HALO AI classifier designed to detect, segment, and quantify tumor and other area across hematoxylin and DAB-stained whole-slide digital images of breast cancer.
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The NSCLC IHC Tumor Detection App is a pre-trained HALO AI classifier designed to detect, segment, and quantify tumor area and non-tumor area across hematoxylin and DAB-stained whole-slide digital images of NSCLC.
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The NSCLC IHC Cancer Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect, segment, and quantify non-cancer cells, IHC-positive cancer cells and IHC-negative cancer cells across hematoxylin and DAB-stained whole-slide digital images of NSCLC.
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The Breast IHC Cancer Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect, segment, and quantify cancer cells and other cells across hematoxylin and DAB-stained whole-slide digital images of breast cancer.
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The NSCLC H&E Cancer Cell Phenotyper App is a pre-trained object phenotyper designed to detect, quantify, and segment cancer cells from non-cancer cells across H&E-stained whole-slide digital images of NSCLC.
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The CRC H&E Cancer Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect, segment, and quantify cancer cells and other cells across H&E-stained whole-slide digital images of colorectal cancer.
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The Pan-Cancer H&E Lymphocyte Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect and quantify lymphocytes across whole slide H&E-stained images of multiple tumor types.
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The Gastric H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass areas across H&E-stained whole slide images of gastric cancer.
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The Head & Neck Squamous Cell Carcinoma (HNSCC) H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass area across H&E-stained whole slide HNSCC images.
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The Non-Small Cell Lung Cancer (NSCLC) H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass area across H&E-stained whole slide images of NSCLC.
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The Ovarian H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass area across H&E-stained whole slide images of ovarian cancer.
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Free Image Analysis
See HALO AI in action on up to three of your images with a free analysis.
Knowledge Center
Learn more about HALO AI by exploring the tabs below. For an introduction to the capabilities of HALO AI, we recommend you check out our HALO AI white paper!
SlideQC Application Note
Read this app note to learn how our AI-powered SlideQC BF quality control algorithm can help enhance efficiency, improve the quality of diagnostic, research, and archival material, and future-proof labs.
Glomeruli Quantification and Characterization 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.
Quantify Single and Multiplexed RNAscope® Probes Across Whole Slide Tissues
Using our ISH module for brightfield and our FISH module for fluorescence, we demonstrate how to quantify ISH signal from RNAscope® assays on a per cell or per unit area basis and output an overall expression score based on ACD Bio’s recommended scoring guidelines.
HiPlex RNAscope™ Application Note
Using our FISH module we demonstrate how to perform 12-plex RNAscope image analysis using ACD’s HiPlexv2 assay.
Identifying Islets with HALO AI
Learn how HALO AI can detect islets of variable morphology in H&E-stained pancreatic tissue
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!
HALO, HALO AI, & HALO Link Cloud Services Brochure
Discover how our Cloud Services team can provide a secure, scalable, and flexible cloud deployment of HALO, HALO AI, and HALO Link.
University of Washington Cloud Services Case Study
Read this case study to learn how partnering with our Cloud Services team has delivered greater scalability and cost efficiency for the BioRepository and Integrated Neuropathology laboratory at University of Washington Medicine.
NCI Case Study
Download this case study to learn about considerations the National Cancer Institute made when planning to migrate its HALO, HALO AI, and HALO Link deployments to the cloud, and benefits the NCI experienced after migration, including 50% faster HALO image analysis.
Cloud Services and AI Diagnostics Case Study
Learn how the optimized, scalable infrastructure provided by Indica Labs Cloud Services enables the rapid development and robust collaboration of our AI Diagnostics group, powering the next generation of automated AI-based decision support tools.
Clustering Workflow eBook
Check out our collaborative eBook with Lunaphore to learn about an optimized workflow for extracting information from hyperplex datasets and illuminating changes in cellular neighborhoods using HALO, HALO AI, and the Lunaphore COMET platform.
Lunaphore COMET Hyperplex IF and HALO® Image Analysis eBook
Our collaborative eBook with Lunaphore shows how HALO and HALO AI, alongside the Lunaphore COMET instrument, enable a high-throughput workflow for phenotypic and spatial analysis of cells in the tumor microenvironment.
Sequential Same Slide mIF and H&E eBook
Reveal more data on a single slide with this workflow for sequential same slide multiplex IF and H&E staining and imaging using HALO and HALO AI.
mIF Co-Registration and Cellular Analysis eBook
Learn how HALO offers a streamlined workflow for co-registration and cellular analysis of immunofluorescence images from cyclically stained and imaged slides.
Qualitative and Quantitative Evaluation of the TME eBook
Check out this ebook to see how AI-based nuclear segmentation and tissue classification can streamline cell phenotyping across tissue compartments and samples.
Characterizing the TME of a Colorectal Adenocarcinoma Sample with the Orion Spatial Biology Platform and HALO Image Analysis
Download our collaborative poster with RareCyte to learn how to analyze a colorectal adenocarcinoma sample with HALO and HALO AI
Lunaphore COMET Hyperplex IF and HALO® Image Analysis of the TME
Download our collaborative poster with Lunaphore to see how HALO and HALO AI, alongside the Lunaphore COMET instrument, enable a high throughput workflow for phenotypic and spatial analysis of cells in the tumor microenvironment.
Clustering Workflow for Advanced Cell Phenotyping of Hyperplex IF Images
Spatial Characterization of the TME with Cell DIVE Multiplexed Imaging and HALO Image Analysis
Quantitative Image Analysis of a Combined FISH-IF Assay
Characterizing the TME Using IMC and HALO Image Analysis
Download this poster to learn how HALO and HALO AI can be used to analyze imaging mass cytometry data.
Automated Tumor Budding Quantification in Colorectal Carcinoma H&E Images
Spatial Multiplex Profiling of Immune Markers with the RNAscope Hiplex v2 Assay
