A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection

TL;DR

This paper introduces a deep learning framework using a U-Net based segmentation neural network for real-time, accurate, and robust detection and tracking of the Vagus nerve in ultrasound scans, effective even with small training datasets.

Contribution

The proposed framework achieves high localization accuracy in ultrasound scans with minimal training data, outperforming existing real-time detection methods.

Findings

Localization precision and recall > 90% with small training sets

Outperforms current state-of-the-art detection networks

Operates in real-time across different subjects and devices

Abstract

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structure in ultrasound scans. Our framework is designed to be accurate and robust across subjects and imaging devices, to operate in real-time, and to not require a large training set. It maintains a localization precision and recall higher than 90% when trained on training sets that are as small as 20% in size of the original training set. The framework backbone is a weakly trained segmentation neural network based on U-Net. We tested the framework on two different ultrasound datasets with the aim to detect and track the Vagus nerve, where it outperformed current state-of-the-art real-time object detection networks.