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Tactile sensor-based real-time clustering for tissue differentiation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Reliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon’s visual aspect and tactile impression of the considered tissue, which is—due to inherent low brain consistency contrast—a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when—as for neuroendoscopy—tactile perception will be damped or—as for surgical robotic applications—will not be a priori existent. Here, we present the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.

Methods

A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E1 and E2 for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate cluster algorithm.

Results

Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.

Conclusions

Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising.

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Acknowledgements

This work was founded by the European Research Council (ERC) Advanced ERC Grant No. 320378–SNLSID (Johan Schoukens). David Oliva Uribe would like to thank the support and motivation of Beatriz Vizcaino. In addition, the authors thank to the personnel of the Slaughter House Anderlecht for kindly providing the ex vivo samples.

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Authors and Affiliations

  1. Department of Neurosurgery, Academic Hospital Cologne-Merheim, Cologne, Germany

    Ralf Stroop & Makoto Nakamura

  2. Department of Engineering Technology (INDI), Vrije Universiteit Brussel, Brussels, Belgium

    Johan Schoukens & David Oliva Uribe

Authors
  1. Ralf Stroop
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  2. Makoto Nakamura
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  3. Johan Schoukens
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  4. David Oliva Uribe
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Correspondence to David Oliva Uribe.

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This article does not contain any studies with human participants performed by any of the authors.

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For this type of study, formal consent is not required. This article does not contain patient data.

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Stroop, R., Nakamura, M., Schoukens, J. et al. Tactile sensor-based real-time clustering for tissue differentiation. Int J CARS 14, 129–137 (2019). https://doi.org/10.1007/s11548-018-1869-5

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  • DOI: https://doi.org/10.1007/s11548-018-1869-5

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