106 research outputs found
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
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 fetal
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
\textit{CyclicLoss}, 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
(state-of-art) to , and a relative error reduction from
to . The mean execution speed of the proposed approach of 289
frames per second makes it suitable for real time clinical use.Comment: 10 pages, 2 figure
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
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 fetal 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 CyclicLoss, 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 0.31mm2 (state-of-art) to 0.09mm2, and a relative error reduction from 8.1% to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use. © Springer Nature Switzerland AG 2018
Real-time diameter of the fetal aorta from ultrasound
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use
Real-time diameter of the fetal aorta from ultrasound
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use
COMFormer: classification of maternal-fetal and brain anatomy using a residual cross-covariance attention guided transformer in ultrasound
Monitoring the healthy development of a fetus requires accurate and timely identification of different maternal-fetal structures as they grow. To facilitate this objective in an automated fashion, we propose a deep-learning-based image classification architecture called the COMFormer to classify maternal-fetal and brain anatomical structures present in two-dimensional fetal ultrasound images. The proposed architecture classifies the two subcategories separately: maternal-fetal (abdomen, brain, femur, thorax, mother's cervix, and others) and brain anatomical structures (trans-thalamic, trans-cerebellum, trans-ventricular, and non-brain). Our proposed architecture relies on a transformer-based approach that leverages spatial and global features by using a newly designed residual cross-variance attention (R-XCA) block. This block introduces an advanced cross-covariance attention mechanism to capture a long-range representation from the input using spatial (e.g., shape, texture, intensity) and global features. To build COMFormer, we used a large publicly available dataset (BCNatal) consisting of 12, 400 images from 1,792 subjects. Experimental results prove that COMFormer outperforms the recent CNN and transformer-based models by achieving 95.64% and 96.33% classification accuracy on maternal-fetal and brain anatomy, respectively
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives
This article reviews the recent advances in intelligent robotic ultrasound
(US) imaging systems. We commence by presenting the commonly employed robotic
mechanisms and control techniques in robotic US imaging, along with their
clinical applications. Subsequently, we focus on the deployment of machine
learning techniques in the development of robotic sonographers, emphasizing
crucial developments aimed at enhancing the intelligence of these systems. The
methods for achieving autonomous action reasoning are categorized into two sets
of approaches: those relying on implicit environmental data interpretation and
those using explicit interpretation. Throughout this exploration, we also
discuss practical challenges, including those related to the scarcity of
medical data, the need for a deeper understanding of the physical aspects
involved, and effective data representation approaches. Moreover, we conclude
by highlighting the open problems in the field and analyzing different possible
perspectives on how the community could move forward in this research area.Comment: Accepted by Annual Review of Control, Robotics, and Autonomous
System
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