Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images

Juul van Boxtel, Vincent Vousten, Josien Pluim, Nastaran Mohammadian Rad

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

7 Citaten (Scopus)

Samenvatting

Ultrasound-guided regional anesthesia (UGRA) can replace general anesthesia (GA), improving pain control and recovery time. This method can be applied on the brachial plexus (BP) after clavicular surgeries. However, identification of the BP from ultrasound (US) images is difficult, even for trained professionals. To address this problem, convolutional neural networks (CNNs) and more advanced deep neural networks (DNNs) can be used for identification and segmentation of the BP nerve region. In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images. A CNN model is employed as a classifier to precisely select the images with the BP region. Then, a U-net or M-net model is used for the segmentation. Our experimental results indicate that the proposed hybrid model significantly improves the segmentation performance over a single segmentation model.

Originele taal-2Engels
Titel29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
UitgeverijEuropean Signal Processing Conference, EUSIPCO
Pagina's1246-1250
Aantal pagina's5
ISBN van elektronische versie9789082797060
DOI's
StatusGepubliceerd - 2021
Evenement29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ierland
Duur: 23 aug. 202127 aug. 2021

Congres

Congres29th European Signal Processing Conference, EUSIPCO 2021
Land/RegioIerland
StadDublin
Periode23/08/2127/08/21

Bibliografische nota

Publisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.

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