AI-Powered DICOM Image Segmentation: A Collaborative Platform for Continuous Expert Feedback
Date
2026-03-01
Authors
Santos-Blázquez, Pablo
Vázquez-Ingelmo, Andrea
García-Holgado, Alicia
García-Peñalvo, Francisco José
Sánchez-Puente, Antonio
Sánchez, P. L.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
his work presents the development of an interactive web platform that integrates deep learning techniques for the segmentation of cardiac ultra-sound (echocardiogram) images. The platform incorporates a Picture Archiving and Communication System (PACS) to facilitate the seamless visualization, anno-tation, and automated processing of DICOM images. The web platform features an intuitive interface that allows healthcare professionals to interactively annotate medical images, providing feedback that directly informs model improvements. The system’s retraining workflow ensures that AI-driven segmentation remains adaptable to real-world clinical needs. These findings underscore the importance of iterative AI model refinement through expert feedback, paving the way for more reliable and personalized medical image analysis.
Description
Keywords
DICOM, Image Analysis, Deep Learning, PACS
Citation
Santos-Blázquez, P., Vázquez-Ingelmo, A., García-Holgado, A., García-Peñalvo, F. J., Sánchez-Puente, A., & Sánchez, P. L. (2026). AI-Powered DICOM Image Segmentation: A Collaborative Platform for Continuous Expert Feedback. In A. Rocha, F. J. García-Peñalvo, C. J. Costa, & R. Gonçalves (Eds.), Proceedings of 20th Iberian Conference on Information Systems and Technologies (CISTI 2025) (Vol. 1, pp. 42–51). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-10929-3_4