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Are you ready to take your lab to the next level?

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GERMAN-ENGINEERED

Advanced Digital Pathology Image Analysis you can count on that won't break the bank or your budget

MIKAIA studio features a suite of powerful apps that will streamline your lab’s research workflow and output. Developed by world-renowned Fraunhofer IIS in Erlangen, Germany, it is your ideal digital pathology software to:

– View;

– Annotate;

– Process, and;

– Analyze

your brightfield and fluorescent whole-slide images (WSIs) or single images.

Download MIKAIA® for FREE today

MIKAIA® lite - view, annotate, and convert slides for free

Robust viewer for Brightfield and FL WSIs
Supports all major WSI formats
Import/Export annotations
Touch Ready - supports drawing annotations with pen
Convert/ crop to SVS or Deepzoom
Easily create datasets for AI training
Decentralized annotation storage - safe to relocate your WSIs any time
Easy installation - start in less than a minute
Runs locally on your computer - analyze your data locally
Live stain unmixing
Hotspot Search
Density Heatmaps
Tissue Detection App
Tile Export App
Annotation Image Export App

MIKAIA studio - includes powerful image analysis apps

All functionality from MIKAIA® lite plus the following:
Affordable - Contact us for pricing
Easy batch processing and analysis of entire datasets
Structured CSV files allowing seamless import to R, Python, Matlab, etc.
FL Marker Correlation Module
REST API to integrate your own AI into the MICAIA App Center
IHC Cell Detection App
HE Cell Detection App
Mask by Color App
Cell-Cell Connections App
FL Colocalization App
FL HER2/neu FISH App
FL Cell Counting App
FL Spot Counting
AI Author App (add-on module)

System Requirements

Easy Installation

Simply download and start using right away. No need to wait for a technician to set up an on-site server.

Keep your data in safe hands -- yours!

WSIs are often larger than 1 GB. Uploading this data to the cloud can take a long time and require a lot of expensive cloud storage space. Instead, analyze your data locally and load WSIs into MIKAIA directly from your network or local folder.

Recommended Hardware

MIKAIA can run on a Windows PC or notebook. MIKAIA® AI apps benefit greatly from a GPU such as NVIDIA GeForce® but will still run on CPU if no GPU available.

- Windows 10 or 11, x86 64-bit (required)
- Minimum 8-core CPU
- Minimum 16 GB RAM
- SSD
- GPU with 4 GB or more RAM and many cores

Top Reasons to Use MIKAIA in your Histopathology Research

Conduct quantitative analyses that are not possible manually

For example, counting all cells, cell-cell connections statistics, accurate measurements, tissue composition, and predicting mutations from standard stains.

Analyze more data to obtain higher statistical significance

Evaluate larger ROIs, an entire WSI or an entire data set comprising hundreds of WSIs.

Analyze slides faster: More data in the same amount of time

You only have limited time for your research. Use it efficiently and do not waste time on manual evaluations. Analyzing entire data sets takes hours---not days.

Analyze slides faster: Be the first to publish your discovery

The clock is ticking. Don't let others beat you to the punch.

Eliminate bias, inter-, and intra-observer variance

Digital analysis yields identical results regardless of time and stress. It will not subconsicously change its evaluation criteria over the course of a larger analysis.

Increase chances of getting your paper accepted using latest digital tech and AI

When scoring manually, reviewers will challenge you and ask why no digital image analysis technology was used.

Don't waste your talent and intelligence on tedious tasks

Let the computer collect the raw data. You draw the conclusions and focus on complicated cases.

Unlock the Power of the MIKAIA studio App Center

Save time by automatically outlining tissue.

This App separates foreground from background. The output is an outline polygon per tissue particle.

Tissue Detection

This App creates data sets from annotations.

Either export one image per annotation, e.g. when annotations mark cells or other small objects that fit into a single image, or divide a large annotation into patches, e.g. when large tissue regions are annotated.

Annotation Image Export

This App exports tiles (aka patches) from a whole-slide image.

The tiles can either be exported at the native resolution or at a user-defined resolution. Attributes such as the slide name or tile coordinates can be coded into the file name according to a user-defined naming scheme.

Tile Export

This App is used to select a tissue area based on its color.

For instance it can be used to mask the chromogen in an IHC scan. The absolute and relative size with respect to the entire tissue will be measured and reported.

Mask by Color

This App carries out a spatial analysis between cell types.

It interprets the sample as a graph where cells are nodes and cell-cell connections are edges. Each cell is connected with its adjacent cells. Then connections are classified by the two cell types it connects. Connections are plotted as a histogram and displayed in a matrix table.

Cell-Cell Connections

This App is used to detect positive and negative cells in nuclear IHC stainings.

It analyzes images by first unmixing them into their two stain components. Detected cells can be subdivided by ROIs (e.g. inside and outside tumor; in bands at increasing distance from tumor margin) and hotspots (per ROI) can be sought.

IHC Cell Detection

This App detects and outlines cells in fluorescence slides.

In contrast to the colocalization App it operates on each channel/marker individually.

FL Cell Counting

This App detects cells in low-power fluorescence slides, where each cell appears as a spot.

It is possible to choose which channel is analyzed within the App. The user can modify various parameters such as the sensitivity or spot diameter. The App counts the cells and computes the cell density.

FL Spot Counting

This App can be used to analyze immunofluorescence slides to carry out a co-expression study.

It requires a DAPI channel plus one or more additional markers. For each detected cell, the App analyzes if the cell expresses the marker, and based on the combination of positive markers assigns the cell to a class.

FL Colocalization

HER2/neu FISH scoring is used to assess whether a HER2 overexpression exists.

Requires a FISH image with three markers: DAPI, HER2, and CEP17. The App first detects nuclei in the DAPI channel and traces the contours. Overlapping nuclei are split. Then, it detects red and green spots inside the nuclei that mark HER2 and CEP17 gene amplifications, respectively.

HER2/neu FISH

This App is used to detect cells in an H&E staining.

Cell segmentation/classification datasets can be created by detecting cells automatically (using this App) and then manually labeling them into different classes (e.g. using the class changer brush) and finally exporting images using the Annotation Image Export App.

HE Cell Detection

Train your own patch-based classifier on your data in three simple steps.

1) Define names of tissue classes you want to distinguish;
2) Annotate a few typical regions for these classes in one or multiple slides;
3) Apply your own classifier on new unseen regions or slides! You just trained your own AI!

MIKAIA supports the abllity to add your trained AI as a new App to the App Center.