TY - CONF
T1 - Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images.
AU - Boxtel, Juul P. A. van
AU - Vousten, Vincent R. J.
AU - Pluim, Josien P. W.
AU - Rad, Nastaran Mohammadian
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.23919/EUSIPCO54536.2021.9616329
DO - 10.23919/EUSIPCO54536.2021.9616329
M3 - Paper
SP - 1246
EP - 1250
ER -