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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning.

Xi Qin, Bohan Wang, David Boegner

IEEE Transactions on Bio-Medical Engineering

|September 29, 2021

Summary

This study introduces a camera-based visual odometry (VO) system to accurately track the position of handheld optical coherence tomography (OCT) probes. The integrated deep learning model enables precise segmentation, aiding in kidney analysis and potentially predicting delayed graft function.

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Area of Science:

Medical Imaging
Biomedical Engineering
Computer Vision

Background:

Optical coherence tomography (OCT) provides high-resolution tissue imaging but lacks precise probe localization.
Accurate spatial information is crucial for correlating OCT images with specific anatomical locations.

Purpose of the Study:

To develop and validate a camera-based localization method for handheld OCT probes.
To implement a deep learning-based segmentation technique for OCT images.

Main Methods:

Visual odometry (VO) and simultaneous mapping and localization (SLAM) were employed for real-time OCT probe tracking.
A deep convolutional neural network (CNN) was utilized for kidney tubule lumen segmentation.

Main Results:

The VO system achieved high accuracy in 1D (0.15 mm MAE) and 2D (0.85 mm MAE) translation tracking.
The segmentation method yielded a Dice coefficient of 0.7.
Statistical analysis showed significant correlations between predicted and actual kidney parameters.

Conclusions:

VO effectively tracks OCT probe location with high accuracy, enabling 3D visualization of OCT data.
Deep learning offers accurate and rapid image segmentation capabilities.
The combined approach shows potential for predicting delayed graft function in kidney transplantation.