Published on Sun Apr 26 2020

Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generation

Aji Resindra Widya, Yusuke Monno, Masatoshi Okutomi, Sho Suzuki, Takuji Gotoda, Kenji Miki

Gastric endoscopy is a standard clinical process that enables medical practitioners to diagnose various lesions inside a patient's stomach. If a lesion is found, it is very important to perceive the location of the lesion relative to the global view of the stomach. Previous research showed that

0
0
0
Abstract

Gastric endoscopy is a standard clinical process that enables medical practitioners to diagnose various lesions inside a patient's stomach. If any lesion is found, it is very important to perceive the location of the lesion relative to the global view of the stomach. Our previous research showed that this could be addressed by reconstructing the whole stomach shape from chromoendoscopic images using a structure-from-motion (SfM) pipeline, in which indigo carmine (IC) blue dye sprayed images were used to increase feature matches for SfM by enhancing stomach surface's textures. However, spraying the IC dye to the whole stomach requires additional time, labor, and cost, which is not desirable for patients and practitioners. In this paper, we propose an alternative way to achieve whole stomach 3D reconstruction without the need of the IC dye by generating virtual IC-sprayed (VIC) images based on image-to-image style translation trained on unpaired real no-IC and IC-sprayed images. We have specifically investigated the effect of input and output color channel selection for generating the VIC images and found that translating no-IC green-channel images to IC-sprayed red-channel images gives the best SfM reconstruction result.

Thu May 30 2019
Computer Vision
3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion
Gastric endoscopy is a common clinical practice that enables medical doctors to diagnose the stomach inside a body. This work addressed to reconstruct the 3D shape of a whole stomach with color texture information generated from a standard monocular endoscope video.
0
0
0
Thu May 18 2017
Computer Vision
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
In ingestible wireless wireless transformative capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract. The use of such devices to generate a precise three-dimensional (3D) mapping of the entire inner organ remains an unsolved problem. This paper presents the first complete pipeline for
0
0
0
Mon Jan 18 2021
Computer Vision
Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations
Optical colonoscopy (OC) has a high miss rate due to a number of factors. We present a framework to visualize the missed regions per-frame. The code, data and trained models will be released via our Computational endoscopy Platform.
3
0
0
Fri Mar 27 2020
Computer Vision
Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation
Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopic (VC), are critical for diagnosing and ultimately removing precursors of colon cancer. Both OC and VC contain structural information, but this information is obscured in OC by additional patient-specific texture and specular highlights. The existing CycleGAN approaches do not handle lossy transformations.
1
0
0
Tue Jun 30 2020
Computer Vision
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
0
0
0
Fri Sep 06 2019
Computer Vision
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
We present a self-supervised learning-based pipeline for dense 3Dreconstruction from full-length monocular endoscopic videos. Our method only relies on unlabeled monocular endoscopic videos and conventional multi-view stereo algorithms.
0
0
0