Machine learning-based colon deformation estimation method for colonoscope tracking

no code implementations • 8 Jun 2018 • Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors.

BIG-bench Machine Learning regression

Colonoscope tracking method based on shape estimation network

no code implementations • 20 Apr 2020 • Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Nassir Navab, Kensaku MORI

We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.

Position

Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks

no code implementations • 20 Apr 2020 • Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.

Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes

no code implementations • 14 Jan 2022 • Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Gen Iinuma, Kazunari Misawa, Shigeru Nawano, Kensaku MORI

In this paper, we propose a semi-automated method for generating VU views of the stomach.