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

TL;DR

This paper introduces a hybrid deep learning approach combining classification and segmentation models to accurately identify and segment brachial plexus nerves in ultrasound images, aiding regional anesthesia procedures.

Contribution

It presents a novel hybrid model that improves nerve segmentation accuracy by combining CNN-based classification with U-net or M-net segmentation models.

Findings

Hybrid model outperforms single segmentation models

Significant improvement in segmentation accuracy

Effective identification of BP nerves in ultrasound images

Abstract

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.