ArticleJournal of imaging informatics in medicine2025
Mainecoon: Implementing an Open-Source Web Viewer for DICOM Whole Slide Images with AI-Integrated PACS for Digital Pathology.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Article
- Report on the 2025 DICOM WSI Connectathon.Journal of pathology informatics · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
The rapid advancement of digital pathology comes with significant challenges due to the diverse data formats from various scanning devices creating substantial obstacles to integrating artificial intelligence (AI) into the pathology imaging workflow. To overcome performance challenges posed by large AI-generated annotations, we developed an open-source project named Mainecoon for whole slide images (WSIs) using the Digital Imaging and Communications in Medicine (DICOM) standard. Our solution incorporates an AI model to detect non-alcoholic steatohepatitis (NASH) features in liver biopsies, validated with the DICOM Workgroup 26 Connectathon dataset. AI-generated results are encoded using the Microscopy Bulk Simple Annotations standard, which provides a standardized method supporting both manual and AI-generated annotations, promoting seamless integration of structured metadata with WSIs. We proposed a method by leveraging streaming and batch processing, significantly improving data loading efficiency, reducing user waiting times, and enhancing frontend performance. The web services of the AI model were implemented via the Flask framework, integrated with our viewer and an open-source medical image archive, Raccoon, with secure authentication provided by Keycloak for OAuth 2.0 authentication and node authentication at the National Cheng Kung University Hospital. Our architecture has demonstrated robustness, interoperability, and practical applicability, addressing real-world digital pathology challenges effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.