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Construction of Extended 3D Field of Views of the Internal Bladder Wall Surface: A Proof of Concept

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3D Research

Abstract

3D extended field of views (FOVs) of the internal bladder wall facilitate lesion diagnosis, patient follow-up and treatment traceability. In this paper, we propose a 3D image mosaicing algorithm guided by 2D cystoscopic video-image registration for obtaining textured FOV mosaics. In this feasibility study, the registration makes use of data from a 3D cystoscope prototype providing, in addition to each small FOV image, some 3D points located on the surface. This proof of concept shows that textured surfaces can be constructed with minimally modified cystoscopes. The potential of the method is demonstrated on numerical and real phantoms reproducing various surface shapes. Pig and human bladder textures are superimposed on phantoms with known shape and dimensions. These data allow for quantitative assessment of the 3D mosaicing algorithm based on the registration of images simulating bladder textures.

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Acknowledgments

This work was sponsored by the Région Lorraine and the Centre National de la Recherche Scientifique (CNRS) under contract PEPS Biotechno et Imagerie de la Santé. The authors would also like to thank the Centre Hospitalier Universitaire Nancy-Brabois for providing pig bladders and Prof. François Guillemin from the Institut de Cancérologie de Lorraine for his expertise in urology.

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Author notes

  1. Achraf Ben-Hamadou

    Present address: Valeo, Driving Assistance Research, 34 rue St-André Z.I. des Vignes, 93012, Bobigny, France

Authors and Affiliations

  1. Université de Lorraine and CNRS, CRAN, 2 avenue de la Forêt de Haye, 54518, Vandœuvre-lès-Nancy, France

    Achraf Ben-Hamadou, Christian Daul & Charles Soussen

Authors
  1. Achraf Ben-Hamadou
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  2. Christian Daul
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  3. Charles Soussen
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Corresponding author

Correspondence to Christian Daul.

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Ben-Hamadou, A., Daul, C. & Soussen, C. Construction of Extended 3D Field of Views of the Internal Bladder Wall Surface: A Proof of Concept. 3D Res 7, 19 (2016). https://doi.org/10.1007/s13319-016-0095-6

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  • DOI: https://doi.org/10.1007/s13319-016-0095-6

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