  Background:
  Tissue Microarrays (TMAs) significantly increase analytical efficiency in
histopathology and large-scale epidemiologic studies by allowing multiple
tissue cores to be scanned on a single slide. The individual cores can be
digitally extracted and then linked to metadata for analysis in a process known
as de-arraying. However, TMAs often contain core misalignments and artifacts
due to assembly errors, which can adversely affect the reliability of the
extracted cores during the de-arraying process. Moreover, conventional
approaches for TMA de-arraying rely on desktop solutions.Therefore, a robust
yet flexible de-arraying method is crucial to account for these inaccuracies
and ensure effective downstream analyses.
  Results:
  We developed TMA-Grid, an in-browser, zero-footprint, interactive web
application for TMA de-arraying. This web application integrates a
convolutional neural network for precise tissue segmentation and a grid
estimation algorithm to match each identified core to its expected location.
The application emphasizes interactivity, allowing users to easily adjust
segmentation and gridding results. Operating entirely in the web-browser,
TMA-Grid eliminates the need for downloads or installations and ensures data
privacy. Adhering to FAIR principles (Findable, Accessible, Interoperable, and
Reusable), the application and its components are designed for seamless
integration into TMA research workflows.
  Conclusions:
  TMA-Grid provides a robust, user-friendly solution for TMA dearraying on the
web. As an open, freely accessible platform, it lays the foundation for
collaborative analyses of TMAs and similar histopathology imaging data.
Availability: Web application: https://episphere.github.io/tma-grid Code:
https://github.com/episphere/tma-grid Tutorial: https://youtu.be/miajqyw4BVk
