Query: ultrasound of baby via deep learning

title: View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning
abstract: Echocardiography provides an important tool for clinicians to observe the function of the heart in real time, at low cost, and without harmful radiation. Automated localization and classification of heart valves enables automatic extraction of quantities associated with heart mechanical function and related blood flow measurements. We propose a machine learning pipeline that uses deep neural networks for separate classification and localization steps. As the first step in the pipeline, we apply view classification to echocardiograms with ten unique anatomic views of the heart. In the second step, we apply deep learning based object detection to both localize and identify the valves. Image segmentation based object detection in echocardiography has been shown in many earlier studies but, to the best of our knowledge, this is the first study that predicts the bounding boxes around the valves along with classification from 2D ultrasound images with the help of deep neural networks. Our object detection experiments suggest that it is possible to localize and identify multiple valves precisely.

title: Improving Image Quality of Single Plane Wave Ultrasound via Deep Learning Based Channel Compounding
abstract: The emergence of data driven approaches such as Deep Learning has led to novel application of various aspects of science and engineering. It has recently entered the field of ultrasound image beamforming. In this work we investigate neural networks tailored to create images of the quality of multiple compounded plane wave excitations from the data of the central angle (0deg) excitation only. The proposed network is used to produce pixel wise weights to weigh a standard delay and sum image from all channel data available to a pixel. It is found to produce higher quality images than the classical reference reconstruction from the 0deg angle data.

title: qULM DL: Quantitative Ultrasound Localization Microscopy via Deep Learning
abstract: Ultrasound localization microscopy (ULM) has been developed in recent years to significantly improve the spatial resolution of ultrasound imaging by localizing the microbubbles (MBs) flowing in microvasculature. Nevertheless, challenges remain in ULM. In our previous work ( Imaging, 10.1109/TMI.2020.2986781) by using a modified sub pixel convolutional neural network (CNN) we have implemented ULM with fast data processing speed, high imaging accuracy, short data acquisition time, and high flexibility characteristics. However, the signal intensities of MBs are not quantitatively recovered, as similar to many other ULM techniques. To overcome this limitation, in this work, we propose a new deep learning (DL) method, termed as qULM DL, to recover the true distributions (positions and intensities) of the MBs in ULM. A series of numerical simulations and in vivo experiment are performed to evaluate the performance of qULM DL. The results show that qULM DL can not only super resolve the complex structures but also quantitatively recover MBs intensities in these structures.

title: VP25.02: Automatic classification of fetal axial brain planes from ultrasound images via deep learning
abstract: autopsy; in three (2.9% an alternative diagnosis was proposed and in five (4.8% the autopsy was considered uninformative. Diagnostic accuracy averege rates for all ultrasonographic diagnoses were sensetivity of 88.9% (42.5% 98.5% specificity of 94.3% (78.1% 97% PPV of 91% (73% 94.2% and NPV of 87.2% (85.2% 93.1%  Our study demonstrates an excellent correlation between dedicated neurosonography and autopsy findings in the field of fetal brain malformations. Despite this high correlation in a considerable number of cases the autopsy adds important information not demonstrable by imaging. The risk of false positive US diagnoses although low should always be considered.

title: Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
abstract: Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D volume reconstruction from freehand US scans require external tracking devices to provide spatial position for every frame. In this paper, we propose a deep contextual learning network (DCL Net) which can efficiently exploit the image feature relationship between US frames and reconstruct 3D US volumes without any tracking device. The proposed DCL Net utilizes 3D convolutions over a US video segment for feature extraction. An embedded self attention module makes the network focus on the speckle rich areas for better spatial movement prediction. We also propose a novel case wise correlation loss to stabilize the training process for improved accuracy. Highly promising results have been obtained by using the developed method. The experiments with ablation studies demonstrate superior performance of the proposed method by comparing against other state of the art methods. Source code of this work is publicly available at https:/github.com/DIAL RPI/FreehandUSRecon.

title: Autonomous image based ultrasound probe positioning via deep learning

title: Colour Doppler ultrasound of temporal arteries for the diagnosis of giant cell arteritis: a multicentre deep learning study.
abstract: Giant cell arteritis (GCA) is the most common systemic vasculitis in adults. In recent years, colour Doppler ultrasound of the temporal arteries (CDU) has proven to be a powerful non invasive diagnostic tool, but its place in the diagnosis of GCA remains to be defined. A limitation of the CDU is the inter operator reproducibility. Image analysis from a different perspective is now possible with the development of artificial intelligence algorithms. We propose to assess this technology for the detection of the halo sign on CDU images.  Three public hospitals retrospectively collected data from 137 patients suspected of having GCA between January 2017 and April 2019. CDU images (n=1,311) were labelled with the VIA software. Three sets (training, validation and test) were created and analysed with a semantic segmentation technique using a U Net convolutional neural network.  The area under the curve (AUC) was 0.931 and 0.835 on the validation and test set, respectively. An image positivity threshold was determined by focusing on the specificity. With this threshold, a specificity of 95% and a sensitivity of 60% were obtained for the test set. The analysis of the false interpretation showed that the acquisition modalities and the presence of thrombus caused confusion for the algorithm.  We propose an automated image analysis tool for GCA diagnosis. The 2018 EULAR guidelines for image acquisition must be respected before generalising this algorithm. After external validation, this tool could be used as an aid for diagnosis, staff training and student education.

title: Segmentation and classification of knee joint ultrasonic image via deep learning
abstract: The knee is one of the most complicated joints in the human body, but it could be easily injured. Ultrasound imaging is an important technology for the diagnosis of the knee disease. To assist doctors in the treatment and reduce errors of judgment, we investigate the segmentation of disease regions and the automated identification of the typical knee joint diseases. First, we use deep learning to segment the Region of Interest (ROI) To solve the mis segmentation and poor edge segmentation that occur when the ultrasound image is directly fed into the deep neural network, an image segmentation framework is proposed that integrates snake preprocessing, dilated convolution to expand the receptive fields, and multi channel learning. Second, due to the small difference in features among various categories of ultrasound images, a hybrid algorithm is proposed based on the Resnet rough classification and quadratic training with graph embedding. Finally, the experiments show that the proposed image segmentation framework achieves 10% greater accuracy than a common segmentation network. By visualizing the feature vectors extracted from the classification network, we verify that the feature vectors are closer on similar images after quadratic training by graph embedding. Employing the optimization with quadratic training, we increase the classification accuracy by 11% compared to the Resnet approach.

title: Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
abstract: Recent advances in deep learning for medical image segmentation demonstrate expert level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of {n} stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented "big" data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with  transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39% and CycleGAN based domain adaptation method (degrading 25% (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state of the art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.

title: Beyond Classical Ultrasound Contrast via Deep Neural Networks
abstract: Classical ultrasound reconstruction applies model driven approaches to obtain ultrasound images from ultrasound raw data. With the emergence of Deep Learning however data driven approaches become feasible and can be explored. These can be used to take shortcuts in the reconstruction, directly learning the relationship between raw data and image data. Even more, entirely new target contrasts can be pursued. In this work we present an approach to train a neural network to reconstruct image data of a classical ultrasound and a novel MR like contrast from the same ultrasound raw data.