Mainecoon: Implementing an Open-Source Web Viewer for DICOM Whole Slide Images with AI-Integrated PACS for Digital Pathology.
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...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 6; pp. 4075 - 4090 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190236352&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190236352 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Dec2025 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190236352 190236352 190236352 10.1007/s10278-025-01425-6 190236352 ppf: 4075 ppct: 15 formats: tig: atl: Mainecoon: Implementing an Open-Source Web Viewer for DICOM Whole Slide Images with AI-Integrated PACS for Digital Pathology. aug: au: Hsu, Chao-Wei Yang, Si-Wei Lee, Yu-Ting Yao, Kai-Hsuan Hsu, Tzu-Hsuan Chung, Pau-Choo Chu, Yuan-Chia Kuo, Chen-Tsung Lien, Chung-Yueh affil: https://ror.org/019z71f50 Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan sug: subj: Pathology, Clinical DICOM Software Information Retrieval Nonalcoholic Fatty Liver Disease Liver Pathology Biopsy ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|