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The authors thank C. Hoyos-Barcelo, G. Perez-de-Arenaza-Pozo and J. C. Puerta-Acevedo for collaborating in the manual annotation of the images. The authors gratefully acknowledge the Universidad Politecnica de Madrid for providing computing resources on the Magerit Supercomputer.This research was funded by an agreement between Comunidad de Madrid (Consejeria de Educacion, Universidades, Ciencia y Portavocia) and Universidad Politecnica de Madrid, to finance research actions on SARS-CoV-2 and COVID-19 disease with the REACT-UE resources of the European Regional Development Funds. This work was also supported by the Ministry of Economy and Competitiveness of Spain under Grants PID2021-128469OB-I00 and TED2021-131688B-I00, and by Comunidad de Madrid, Spain. Universidad Politecnica de Madrid supports J. D. Arias-Londono through a Maria Zambrano UP2021-035 grant funded by European Union-NextGenerationEU. The authors also thank the Madrid ELLIS unit (European Laboratory for Learning & Intelligent Systems) for its indirect support.

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Automatic semantic segmentation of the osseous structures of the paranasal sinuses

Publicado en:Computerized Medical Imaging And Graphics. 123 102541- - 2025-07-01 123(), DOI: 10.1016/j.compmedimag.2025.102541

Autores: Sun, Yichun; Guerrero-Lopez, Alejandro; Arias-Londono, Julian D; Godino-Llorente, Juan I

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Endoscopic sinus and skull base surgeries require the use of precise neuronavigation techniques, which may take advantage of accurate delimitation of surrounding structures. This delimitation is critical for robotic-assisted surgery procedures to limit volumes of no resection. In this respect, an accurate segmentation of the osseous structures of the paranasal sinuses is a relevant issue to protect critical anatomic structures during these surgeries. Currently, manual segmentation of these structures is a labour-intensive task and requires wide expertise, often leading to inconsistencies. This is due to the lack of publicly available automatic models specifically tailored for the automatic delineation of the complex osseous structures of the paranasal sinuses. To address this gap, we introduce an open source dataset and a UNet SwinTR model for the segmentation of these complex structures. The initial model was trained on nine complete ex vivo CT scans of the paranasal region and then improved with semi-supervised learning techniques. When tested on an external dataset recorded under different conditions, it achieved a DICE score of 98.25 +/- 0.9. These results underscore the effectiveness of the model and its potential for broader research applications. By providing both the dataset and the model publicly available, this work aims to catalyse further research that could improve the precision of clinical interventions of endoscopic sinus and skull-based surgeries.

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