Computers in biology and medicine · 2026
PARASIDE: An automatic paranasal sinus segmentation and structure analysis tool for magnetic resonance imaging.
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Möller Hendrik, Krautschick Lukas, Graf Robert, Atad Matan, Busch Chia-Jung, Beule Achim Georg, Scharf Christian, Kaderali Lars, Menze Bjoern, Rueckert Daniel, Kirschke Jan S, Paperlein Fabian
Аннотация
BACKGROUND: Chronic rhinosinusitis (CRS) is a common and persistent sinus inflammation that affects 5%-12% of the general population. It substantially reduces quality of life, yet its severity is often challenging to assess objectively. The Lund-Mackay score (LMS) rates sinus opacification but is typically assessed manually and subjectively.
METHODS: We introduce Paranasal Segmentation for Imaging-based Disease Evaluation (PARASIDE), an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenoidalis, and ethmoidalis in T1-weighted magnetic resonance imaging. Utilizing that segmentation, we quantify feature relations such as volume, thickness, and intensity relations which were previously observed only manually and subjectively. Using these features, we regress the Total Lund-Mackay Score (TLMS) of each subject. We compare our approach against established baselines: the Quantitative Opacification Score (QOS) and the Quantitative Lund-Mackay Score (QLMS).
RESULTS: PARASIDE achieves a mean-squared error (MSE) of 2.444 and mean absolute error (MAE) of 1.181 for TLMS prediction, outperforming the QOS/QLMS baseline (MSE = 3.784, MAE = 1.445). The segmentation achieves a mean Dice similarity coefficient of 0.882 ± 0.138 and an average symmetric surface distance (ASSD) of 0.311 ± 0.354 mm across all structures.
CONCLUSION: 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.
METHODS: We introduce Paranasal Segmentation for Imaging-based Disease Evaluation (PARASIDE), an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenoidalis, and ethmoidalis in T1-weighted magnetic resonance imaging. Utilizing that segmentation, we quantify feature relations such as volume, thickness, and intensity relations which were previously observed only manually and subjectively. Using these features, we regress the Total Lund-Mackay Score (TLMS) of each subject. We compare our approach against established baselines: the Quantitative Opacification Score (QOS) and the Quantitative Lund-Mackay Score (QLMS).
RESULTS: PARASIDE achieves a mean-squared error (MSE) of 2.444 and mean absolute error (MAE) of 1.181 for TLMS prediction, outperforming the QOS/QLMS baseline (MSE = 3.784, MAE = 1.445). The segmentation achieves a mean Dice similarity coefficient of 0.882 ± 0.138 and an average symmetric surface distance (ASSD) of 0.311 ± 0.354 mm across all structures.
CONCLUSION: 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.