Szczegóły publikacji
Opis bibliograficzny
Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle–tissue interaction / Katarzyna HERYAN, Witold Serwatka, Dominik RZEPKA, Patricio Fuentealba, Michael FRIEBE // International Journal of Computer Assisted Radiology and Surgery ; ISSN 1861-6410. — 2025 — vol. 20 iss. 9 spec. iss., s. 1795–1806. — Bibliogr. s. 1805–1806, Abstr. — Publikacja dostępna online od: 2025-08-12. — 39th International Congress and Exhibition on Computer Assisted Radiology and Surgery
Autorzy (5)
- AGHHeryan Katarzyna
- AGHSerwatka Witold
- AGHRzepka Dominik
- Fuentealba Patricio
- AGHFriebe Michael
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 163208 |
|---|---|
| Data dodania do BaDAP | 2025-10-04 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s11548-025-03491-1 |
| Rok publikacji | 2025 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | International Journal of Computer Assisted Radiology and Surgery |
Abstract
Purpose: Numerous medical procedures, such as pharmaceutical fluid injections and biopsies, require the use of a surgical needle. During such procedures, the localization of the needle is of prime importance, both to ensure that no vital organs will be or have been damaged and to confirm that the target location has been reached. The guidance to a target and its localization is done using different imaging devices, such as MRI machines, CT scans, and US devices. All of them suffer from artifacts, making the accurate localization, especially the tip, of the needle difficult. This implies the necessity for a new needle guidance technique. Methods: The movement of a needle through human tissue produces vibroacoustic signals which may be leveraged to retrieve information on the needle’s location using data processing and deep learning techniques. We have constructed a specialized phantom with animal tissue submerged in gelatine to gather the data needed to prove this hypothesis. Results and conclusion: 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. © The Author(s) 2025.
