Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy

  • Esmitt RamírezEmail author
  • Carles Sánchez
  • Agnés Borràs
  • Marta Diez-Ferrer
  • Antoni Rosell
  • Debora Gil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11041)


Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.


Biopsy guiding Bronchoscopy Lung biopsy Intervention guiding Airway codification 



This work was supported by Catalan, Spanish and European projects DPI2015-65286-R, 2014-SGR-1470, CERCA Programme/Generalitat de Catalunya. Also, the first author holds the fellowship number BES-2016-078042 granted by the Ministry of Economy, Industry and Competitiveness, Spain. A Titan X Pascal was used for this research, donated by NVIDIA. Debora Gil is part of the Serra Hunter programme.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computer Vision CenterAutonomous University of BarcelonaBarcelonaSpain
  2. 2.Bellvitge University HospitalBarcelonaSpain

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