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An open-source platform for structured annotation and computational workflows in digital pathology research.

Luca Lianas1, Mauro Del Rio2, Luca Pireddu2

  • 1Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy. luca.lianas@crs4.it.

Scientific Reports
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

Digital pathology advancements enable AI tools, but current annotation methods lack structure. The CRS4 Digital Pathology Platform (CDPP) offers structured, multi-label annotation for high-quality, reproducible digital pathology research datasets.

Keywords:
Annotation ProtocolComputational PathologyComputational ProvenanceDigital PathologyHistopathologyWSI Annotation

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical informatics

Background:

  • Digital pathology enables large-scale data acquisition for AI tools and clinical research.
  • Current open-source annotation tools for whole-slide images (WSI) use single-labeling, limiting structured data representation.
  • Lack of protocol adherence and provenance tracking in annotation leads to data variability and reduced reproducibility.

Purpose of the Study:

  • To introduce the CRS4 Digital Pathology Platform (CDPP), an open-source system for structured annotation of WSI.
  • To address limitations in current annotation tools by enabling multi-label, protocol-driven data collection.
  • To enhance the quality, reproducibility, and reusability of digital pathology datasets for research.

Main Methods:

  • Development of the CRS4 Digital Pathology Platform (CDPP) with features for structured, multi-label annotation.
  • Implementation of customizable annotation protocols and dedicated tools for accuracy and efficiency.
  • Integration of workflow-based computational analysis with provenance tracking.

Main Results:

  • The CDPP supports structured, multi-label morphological and clinical image annotation.
  • The platform facilitates adherence to controlled, customizable annotation protocols.
  • Integrated provenance tracking ensures workflow reproducibility and data reusability.
  • Successful application in three distinct research studies, demonstrating robust performance.

Conclusions:

  • The CDPP effectively assists pathologists in generating high-quality, structured annotated datasets.
  • The platform enhances accuracy, efficiency, and consistency in the digital pathology annotation process.
  • CDPP promotes reproducibility and reusability of data for digital pathology research.