text: The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fet al abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called , to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from @math (state-of-art) to @math , and a relative error reduction from @math to @math . The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.
  @cite_3: We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fet al heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fet al heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for improving localization accuracy. (iii) a recurrent architecture is designed to recursively compute regional convolutional features temporally over sequential frames, allowing each prediction to be conditioned on the whole video. This results in a spatial-temporal model that precisely describes detailed heart parameters in challenging US videos. We report results on a real-world clinical dataset, where our method achieves performance on par with expert annotations.
  @cite_17: Accurate acquisition of fet al ultrasound US standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fet al anatomy and intensive manual labors. Hence, automatic approaches are highly demanded in clinical practice. However, automatic detection of standard planes containing key anatomical structures from US videos remains a challenging problem due to the high intra-class variations of standard planes. Unlike previous studies that developed specific methods for different anatomical standard planes respectively, we present a general framework to detect standard planes from US videos automatically. Instead of utilizing hand-crafted visual features, our framework explores spatio-temporal feature learning with a novel knowledge transferred recurrent neural network T-RNN, which incorporates a deep hierarchical visual feature extractor and a temporal sequence learning model. In order to extract visual features effectively, we propose a joint learning framework with knowledge transfer across multi-tasks to address the insufficiency issue of limited training data. Extensive experiments on different US standard planes with hundreds of videos corroborate that our method can achieve promising results, which outperform state-of-the-art methods.
summary: However, the exploitation of temporal redundancy on US sequences was shown to be a solution for improving overall detection results of the fet al heart @cite_3 , where the use of a CNN coupled with a recurrent neural network (RNN) is strategic. Other works, propose similar approach in order to detect the presence of standard planes from prenatal US data using CNN with Long-Short Term Memory (LSTM) @cite_17 .