Abstract
This paper considers the problem of airway tree matching in 3D images. An algorithm dedicated to chest scans of one patient obtained at different breathing stages is proposed. It assumes that the airway trees were already segmented from the 3D images. The method gradually transforms the moving tree to match a fixed tree, recursively rotating consecutive branches and their subbranches. The experiments were performed on 3D CT datasets of three patients, with images representing lungs in successive breathing stages. The assessment was made via the DICE coefficient between the fixed and the moving tree. Due to transformation, the coefficient increased by up to 30%. These results show that in the case of one patient, the proposed method can be an alternative to classical image registration approaches.
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Acknowledgement
The authors would like to acknowledge Prof. Maciej Orkisz from University Claude Bernard Lyon 1 (CREATIS Lab), and Prof. Jean-Christophe Richard from Hospices Civils de Lyon for introduction into the subject of lung analysis in patients with ARDS and providing 3D CT lung images at different breathing stages used in this work.
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Kucharski, A., Fabijańska, A. (2022). An Algorithm for Matching Binary Airway Trees in 3D Images. In: Piaseczna, N., Gorczowska, M., Łach, A. (eds) Innovations and Developments of Technologies in Medicine, Biology and Healthcare. EMBS ICS 2020. Advances in Intelligent Systems and Computing, vol 1360. Springer, Cham. https://doi.org/10.1007/978-3-030-88976-0_8
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DOI: https://doi.org/10.1007/978-3-030-88976-0_8
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