Fetal Brain Imaging: A Composite Neural Network Approach for Keyframe Detection in Ultrasound Videos

TL;DR

This paper introduces a composite neural network combining CNN and RNN for keyframe detection in fetal brain ultrasound videos, aiming to enhance analysis efficiency and accuracy.

Contribution

It proposes a novel neural network architecture specifically designed for fetal brain ultrasound keyframe detection, integrating spatial and temporal features.

Findings

Potential to improve fetal brain ultrasound analysis accuracy

Supports earlier detection and diagnosis of fetal brain conditions

Enhances efficiency of ultrasound video analysis

Abstract

This article presents a novel approach to keyframe detection in ultrasound videos, with a particular focus on fetal brain imaging. The proposed model is a composite neural network architecture that combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN). The CNN extracts spatial features from individual video frames, while the RNN captures temporal dependencies between consecutive frames within each video sequence. The proposed model may improve the efficiency and accuracy of fetal brain ultrasound analysis, thereby supporting earlier detection, diagnosis, and treatment planning for selected fetal brain conditions.