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Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

2018

Article

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Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.

Author(s): Turan, Mehmet and Pilavci, Yusuf Yigit and Ganiyusufoglu, Ipek and Araujo, Helder and Konukoglu, Ender and Sitti, Metin
Journal: Machine Vision and Applications
Volume: 29
Number (issue): 2
Pages: 345--359
Year: 2018
Month: Febuary
Day: 01

Department(s): Physical Intelligence
Bibtex Type: Article (article)

DOI: 10.1007/s00138-017-0905-8
URL: https://doi.org/10.1007/s00138-017-0905-8

BibTex

@article{Turan2018,
  title = {Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots},
  author = {Turan, Mehmet and Pilavci, Yusuf Yigit and Ganiyusufoglu, Ipek and Araujo, Helder and Konukoglu, Ender and Sitti, Metin},
  journal = {Machine Vision and Applications},
  volume = {29},
  number = {2},
  pages = {345--359},
  month = feb,
  year = {2018},
  url = {https://doi.org/10.1007/s00138-017-0905-8},
  month_numeric = {2}
}