Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle–tissue interaction

This paper summarizes our initial experiments, in which we preprocessed the data, converted it into two different spectrogram representations (Mel and continuous wavelet transform spectrograms), and used them as input for two different deep learning models: NeedleNet and ResNet-34. The goal of this work was to chart out an optimal direction for further research.

Authors

Michael Friebe (ORCID: https://orcid.org/0000-0002-8624-0800)
Dominik Rzepka (ORCID: https://orcid.org/0000-0002-1159-2502)
Katarzyna Heryan (ORCID: https://orcid.org/0000-0001-8104-7828)
Patricio Fuentealba (ORCID: https://orcid.org/0000-0002-7119-0580)
Witold Serwatka

Institutions

Austral University of Chile (CL)
University of Agriculture in Krakow (PL)
AGH University of Krakow (PL)

Publication Details

Journal: International Journal of Computer Assisted Radiology and Surgery
Published: 2025-08-12
DOI: https://doi.org/10.1007/s11548-025-03491-1
Primary Topic: Photoacoustic and Ultrasonic Imaging
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Deutsche Forschungsgemeinschaft
Akademia Górniczo-Hutnicza im. Stanislawa Staszica

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article

Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle–tissue interaction

Michael Friebe, Dominik Rzepka, Katarzyna Heryan, Patricio Fuentealba et al.

Aug 12, 2025

International Journal of Computer Assisted Radiology and Surgery

Photoacoustic and Ultrasonic Imaging

article

Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle–tissue interaction

Michael Friebe, Dominik Rzepka, Katarzyna Heryan, Patricio Fuentealba, Witold Serwatka

August 12, 2025article en

Abstract

This paper summarizes our initial experiments, in which we preprocessed the data, converted it into two different spectrogram representations (Mel and continuous wavelet transform spectrograms), and used them as input for two different deep learning models: NeedleNet and ResNet-34. The goal of this work was to chart out an optimal direction for further research.

International Journal of Computer Assisted Radiology and SurgeryVol. 20(9)

Austral University of Chile (CL), University of Agriculture in Krakow (PL), AGH University of Krakow (PL)

Deutsche Forschungsgemeinschaft, Akademia Górniczo-Hutnicza im. Stanislawa Staszica

Openalex Percentile: Top 81%

Photoacoustic and Ultrasonic Imaging

DOI Open Access PDF OpenAlex

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Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle–tissue interaction — Michael Friebe, Dominik Rzepka, et al. · International Journal of Computer Assisted Radiology and Surgery (2025) | TGRS Research Map | TGRS