PARASIDE: An automatic paranasal sinus segmentation and structure analysis tool for magnetic resonance imaging
PARASIDE enables the first automated whole-paranasal sinus segmentation for T1-weighted MRI, extracting quantitative features that predict CRS severity more accurately than existing volumetric scoring methods. By integrating high-quality segmentation with fully automated TLMS estimation, our system offers a reproducible and objective assessment tool in clinical workflows, with the potential to reduce inter-rater variability, accelerate reporting, and support large-scale retrospective studies.
Authors
- Matan Atad (ORCID: https://orcid.org/0000-0001-6952-517X)
- Chia‐Jung Busch (ORCID: https://orcid.org/0000-0001-8254-8237)
- Robert Graf (ORCID: https://orcid.org/0000-0001-6656-3680)
- Lars Kaderali (ORCID: https://orcid.org/0000-0002-2359-2294)
- Lukas Krautschick
- Fabian Paperlein
- Achim Georg Beule
- Bjoern Menze
- Christian Scharf
- Hendrik Möller
- Daniel Rueckert
- Jan S. Kirschke
Institutions
- ZHAW Zurich University of Applied Sciences (CH)
- University of Zurich (CH)
- Universitätsmedizin Greifswald (DE)
- TUM Klinikum (DE)
- Universität Greifswald (DE)
- Leibniz Institute for Plasma Science and Technology (DE)
- Imperial College London (GB)
- Technical University of Munich (DE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1016/j.compbiomed.2026.111511
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00