As hyperplexing and AI-based tissue segmentation and classification continue to gain prevalence in digital pathology, researchers are interpreting more data than ever before. At Indica Labs, our HALO platforms are optimized for ease-of-use as well as powerful, accurate analysis, and include multiple features to help users get the most from their data. In this blog post, we outline the benefits and application of interactive markup images, a powerful tool for more easily exploring the details of analysis results generated by HALO®, HALO AI, and HALO Link. 

The Basics of Interactive Markup Images 

Unlike conventional markup images, which display a static output, interactive markup images are dynamic, allowing users to toggle on and off different populations of interest as they view results. Beyond switching specific populations on and off, users can also change markup colors, toggle cell borders, and in some cases adjust markup contrast. This functionality is possible because interactive markups are written by HALO platforms as multi-layered OME.TIFF files, which also enables analysis outside of HALO with any third-party platform that supports this format. Every markup element is saved as a discrete layer and has channel metadata in the file, and for this reason we use “channel” in this blog to refer to elements of all kinds. 

When users generate an interactive markup image in a HALO platform, a markup legend is also created that displays the available channels and facilitates user interaction. Depending on the analysis context, channels could include various protein or probe markups or AI model-generated classes.  

The Benefits of Going Interactive 

With the ability to toggle channels on and off and adjust their appearance, interactive markup images offer significant advantages over static markup images. By allowing users to visualize all channel combinations following a single analysis run, interactive markups can streamline workflows and save time, even while promoting fuller understanding of results. This is particularly useful for workflows involving: 

• Understanding colocalization and combined phenotypes: Users can easily switch between visualizing different populations, making it easier to assess how various biomarkers or cell types relate and interact. 

• Exploring spatial relationships: Interactive markups provide a streamlined method to qualitatively examine spatial relationships between populations of interest before diving into more in-depth quantitative analysis. 

• Developing AI classifiers: The ability to toggle channels and adjust visual parameters aids in refining probability thresholds and optimizing classifier performance. 

Though interactive markup images offer significant benefits compared to static markups in these and other applications, they do come with the drawback of larger file size. Importantly, users still have the option to generate conventional markup images, saving file space when a static markup is sufficient.