Evidence map›Paper›PMID 32700243›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

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

M Gromniak, N Gessert, T Saathoff, A Schlaefer

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Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

M GromniakInstitute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. martin.gromniak@tuhh.de.ORCID http://orcid.org/0000-0002-6602-8685
N GessertInstitute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
T SaathoffInstitute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.
A SchlaeferInstitute of Medical Technology, Hamburg University of Technology, Hamburg, Germany.

Funding

Deutsche Forschungsgemeinschaft (DE) SCHL 1844/2-1Deutsche Forschungsgemeinschaft (DE) SCHL 1844/2-2Technische Universität Hamburg i3
6 · The paper itself

Abstract

purposeNeedle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g., during robotic needle placement.

methodsWe investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal and forces. For this purpose, we consider three different needles with a new, more robust design which are calibrated using convolutional neural networks (CNNs). We compare training the CNNs with the raw OCT signal and the reconstructed depth profiles.

resultsWe find that using raw data as an input for the largest CNN model outperforms the use of reconstructed data with a mean absolute error of 5.81 mN compared to 8.04 mN.

conclusionsWe find that deep learning with raw spectral OCT data can improve learning for the task of force estimation. Our needle design and calibration approach constitute a very accurate fiber-optical sensor for measuring forces at the needle tip.

Indexed as

Deep LearningFeedbackNeural Networks, ComputerRoboticsTomography, Optical CoherenceCalibrationHumansImage Processing, Computer-AssistedMechanical PhenomenaNeedlesDeep learningForce estimationOptical coherence tomographyRaw data

Identifiers

PMID32700243
PMCPMC7502064

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.