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A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer.

Pau Xiberta1,2, Marc Ruiz3, Màrius Vila3

  • 1Graphics and Imaging Laboratory, Universitat de Girona, Girona, 17003, Catalonia. pau.xiberta@udg.edu.

Journal of Imaging Informatics in Medicine
|March 2, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel method for integrating deep learning (DL) segmentation models directly into DICOM viewers. This approach simplifies clinical adoption by embedding DL tools within existing workflows, enhancing medical imaging diagnostics.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Deep learning (DL) models show promise for automating medical imaging diagnostics.
  • Clinical integration of DL solutions remains a significant challenge due to workflow complexity and technical barriers.

Purpose of the Study:

  • To propose and validate a native integration strategy for DL segmentation models within a DICOM viewer.
  • To enhance clinical adoption and usability of DL tools in medical imaging workflows.

Main Methods:

  • Developed a native integration strategy embedding DL model execution directly into a CE-marked open-source DICOM viewer.
  • Created a dedicated DL module within the viewer, eliminating the need for external software or complex APIs.
  • Validated the approach using vertebral bodies and liver segmentation use cases with models from different DL libraries.
Keywords:
DICOM viewersDeep learningImage segmentationModel standardisationOpen sourceRadiological workflow

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Main Results:

  • The native integration strategy allows direct use of DL segmentation models within the DICOM viewer.
  • The method is compatible with heterogeneous DL architectures and requires minimal user interaction.
  • Clinical usability is preserved without disrupting existing diagnostic workflows.

Conclusions:

  • The proposed methodology offers a simple, flexible, and clinically ready solution for viewer-native DL deployment.
  • This approach facilitates the adoption of DL tools in regulated healthcare environments.
  • Enables efficient sharing and reuse of DL models across institutions, advancing medical imaging diagnostics.