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Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

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High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements

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Needle tip force estimation by deep learning from raw spectral OCT data.

M Gromniak¹, N Gessert², T Saathoff²

¹Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. martin.gromniak@tuhh.de.

International Journal of Computer Assisted Radiology and Surgery

|July 24, 2020

Summary

This summary is machine-generated.

Using raw spectral Optical Coherence Tomography (OCT) data with deep learning improves force estimation accuracy for fiber-optical sensors integrated into needles during robotic procedures. This enhanced calibration method offers precise feedback for needle navigation.

Keywords:

Deep learning Force estimation Optical coherence tomography Raw data

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Area of Science:

Biomedical Engineering
Optical Sensing Technologies
Robotics in Medicine

Background:

Accurate needle placement is critical for medical procedures like biopsies and brachytherapy.
Fiber-optical sensors integrated into needle tips can provide crucial force feedback for navigation.
Optical Coherence Tomography (OCT) is a valuable imaging modality for visualizing tissue during interventions.

Purpose of the Study:

To investigate the calibration of OCT for sensing forces during robotic needle placement.
To determine if using raw spectral OCT data improves deep learning-based force estimation compared to reconstructed OCT data.

Main Methods:

Development of three novel, robust needle designs with integrated fiber-optical sensors.
Calibration of sensors using Convolutional Neural Networks (CNNs) trained on OCT data.
Comparison of CNN performance using raw spectral OCT signals versus reconstructed depth profiles.

Main Results:

The largest CNN model, when trained with raw spectral OCT data, achieved a lower mean absolute error (5.81 mN) compared to using reconstructed data (8.04 mN).
This demonstrates superior performance of raw data input for force estimation.

Conclusions:

Deep learning utilizing raw spectral OCT data enhances the accuracy of force estimation for needle-tip sensors.
The developed needle design and calibration method provide a highly accurate fiber-optical sensing solution for measuring forces during needle navigation.