Simultaneous shape and camera-projector parameter estimation for 3D endoscopic system using CNN-based grid-oneshot scan

Ryo Furukawa, Genki Nagamatsu, Shiro Oka, Takahiro Kotachi, Yuki Okamoto, Shinji Tanaka, Hiroshi Kawasaki

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

For effective in situ endoscopic diagnosis and treatment, measurement of polyp sizes is important. For this purpose, 3D endoscopic systems have been researched. Among such systems, an active stereo technique, which projects a special pattern wherein each feature is coded, is a promising approach because of simplicity and high precision. However, previous works of this approach have problems. First, the quality of 3D reconstruction depended on the stabilities of feature extraction from the images captured by the endoscope camera. Second, due to the limited pattern projection area, the reconstructed region was relatively small. In this Letter, the authors propose a learning-based technique using convolutional neural networks to solve the first problem and an extended bundle adjustment technique, which integrates multiple shapes into a consistent single shape, to address the second. The effectiveness of the proposed techniques compared to previous techniques was evaluated experimentally.

Original languageEnglish
Pages (from-to)249-254
Number of pages6
JournalHealthcare Technology Letters
Volume6
Issue number6
DOIs
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Health Information Management

Fingerprint Dive into the research topics of 'Simultaneous shape and camera-projector parameter estimation for 3D endoscopic system using CNN-based grid-oneshot scan'. Together they form a unique fingerprint.

Cite this