The Beatles' 1967 album Sgt. Pepper's Lonely Hearts Club Band has a widely recognized album cover that depicts several dozen celebrities and other images. The image was made by posing the Beatles in front of life-sized, black-and-white photographs pasted onto hardboard and hand-tinted.[1]
Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.
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Cardiovascular disease is a leading cause of death in the United States. The identification of cardiac diseases on conventional three-dimensional (3D) CT can have many clinical applications. An automated method that can distinguish between healthy and diseased hearts could improve diagnostic speed and accuracy when the only modality available is conventional 3D CT. In this work, we proposed and implemented convolutional neural networks (CNNs) to identify diseased hears on CT images. Six patients with healthy hearts and six with previous cardiovascular disease events received chest CT. After the left atrium for each heart was segmented, 2D and 3D patches were created. A subset of the patches were then used to train separate convolutional neural networks using leave-one-out cross-validation of patient pairs. The results of the two neural networks were compared, with 3D patches producing the higher testing accuracy. The full list of 3D patches from the left atrium was then classified using the optimal 3D CNN model, and the receiver operating curves (ROCs) were produced. The final average area under the curve (AUC) from the ROC curves was 0.840 0.065 and the average accuracy was 78.9% 5.9%. This demonstrates that the CNN-based method is capable of distinguishing healthy hearts from those with previous cardiovascular disease.
An echocardiogram is a noninvasive (the skin is not pierced) procedure used to assess the heart's function and structures. During the procedure, a transducer (like a microphone) sends out sound waves at a frequency too high to be heard. When the transducer is placed on the chest at certain locations and angles, the sound waves move through the skin and other body tissues to the heart tissues, where the waves bounce or "echo" off of the heart structures. These sound waves are sent to a computer that can create moving images of the heart walls and valves.
3-D (three-dimensional) echocardiography. 3-D echo technique captures three-dimensional views of the heart structures with greater detail than 2-D echo. The live or "real time" images allow for a more accurate assessment of heart function by using measurements taken while the heart is beating. 3-D echo shows enhanced views of the heart's anatomy and can be used to determine the appropriate plan of treatment for a person with heart disease.
You will be connected to an ECG monitor that records the electrical activity of the heart and monitors the heart during the procedure using small, adhesive electrodes. The ECG tracings that record the electrical activity of the heart will be compared with the images displayed on the echocardiogram monitor.
The technologist will place warmed gel on your chest and then place the transducer probe on the gel. You will feel a slight pressure as the technologist positions the transducer to get the desired images of your heart.
During the test, the technologist will move the transducer probe around and apply varying amounts of pressure to get images of different locations and structures of your heart. The amount of pressure behind the probe should not be uncomfortable. If it does make you uncomfortable, let the technologist know. You may be asked to hold your breath, take deep breaths, or even sniff through your nose during the procedure.
The researchers had to develop sophisticated new tools that accounted for the gas movement around Sgr A*. While M87* was an easier, steadier target, with nearly all images looking the same, that was not the case for Sgr A*. The image of the Sgr A* black hole is an average of the different images the team extracted, finally revealing the giant lurking at the centre of our galaxy for the first time.
Scientists are particularly excited to finally have images of two black holes of very different sizes, which offers the opportunity to understand how they compare and contrast. They have also begun to use the new data to test theories and models of how gas behaves around supermassive black holes. This process is not yet fully understood but is thought to play a key role in shaping the formation and evolution of galaxies.
Progress on the EHT continues: a major observation campaign in March 2022 included more telescopes than ever before. The ongoing expansion of the EHT network and significant technological upgrades will allow scientists to share even more impressive images as well as movies of black holes in the near future.
The Event Horizon Telescope (EHT) Collaboration has created a single image (top frame) of the supermassive black hole at the centre of our galaxy, called Sagittarius A* (or Sgr A* for short), by combining images extracted from the EHT observations.
The images can also be clustered into four groups based on similar features. An averaged, representative image for each of the four clusters is shown in the bottom row. Three of the clusters show a ring structure but, with differently distributed brightness around the ring. The fourth cluster contains images that also fit the data but do not appear ring-like.
The bar graphs show the relative number of images belonging to each cluster. Thousands of images fell into each of the first three clusters, while the fourth and smallest cluster contains only hundreds of images. The heights of the bars indicate the relative "weights," or contributions, of each cluster to the averaged image at top.
Note: Unless specifically noted, the images and videos of the EHT, along with the texts of press releases, announcements, pictures of the week, blog posts and captions, are licensed under a Creative Commons Attribution 4.0 International License, and may on a non-exclusive basis be reproduced without fee provided the credit is clear and visible. The license allows adaptation of the material, but any adaptations do not affect or impair the use of the original EHT material by others under Creative Commons license.
Embryo hearts show evolution of the heart from a three-chambered in frogs to a four-chambered in mammals.
Credit: Zina Deretsky, National Science Foundation after Benoit Brueau, the Gladstone Institute of Cardiovascular Disease
Benoit Bruneau of the Gladstone Institute of Cardiovascular Disease explains the discovery of the first genetic link in the evolution of the heart from three- to four-chambered. He walks through the anatomy of the cold-blooded frog heart that has three chambers; talks about its differences with the warm-blooded four-chambered heart, and explains some evolutionary advantages of being warm-blooded. He explains the molecular pattern of the protein Tbx5 and how it is different in embryo frog hearts compared with embryo mammal hearts. When the protein is present throughout the entire heart, three chambers form. However, when Tbx5 is restricted only to the left side of the heart, then the wall separating the two ventricles forms and four chambers result. When irregularities in the amounts of the protein occur in human babies, congenital heart defects of the septum result.
Credit: National Science Foundation/Gladstone Institute
The images are in the public domain and thus free of any copyright restrictions. As a matter of courtesy we request that the content provider (Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities) be credited and notified in any public or private usage of this image.
I have a shared Google Photos album with thousands of images where several people marked which images they liked using the heart button. I want to collect all of these images, but I don't see any way to do it other than going through each image manually. Is there a way to do this more efficiently?
Cardiac MR acquisition with complete coverage from base to apex is required to ensure accurate subsequent analyses, such as volumetric and functional measurements. However, this requirement cannot be guaranteed when acquiring images in the presence of motion induced by cardiac muscle contraction and respiration. To address this problem, we propose an effective two-stage pipeline for detecting and synthesising absent slices in both the apical and basal region. The detection model comprises several dense blocks containing convolutional long short-term memory (ConvLSTM) layers, to leverage through-plane contextual and sequential ordering information of slices in cine MR data and achieve reliable classification results. The imputation network is based on a dedicated conditional generative adversarial network (GAN) that helps retain key visual cues and fine structural details in the synthesised image slices. The proposed network can infer multiple missing slices that are anatomically plausible and lead to improved accuracy of subsequent analyses on cardiac MRIs, e.g., ventricle segmentation, cardiac quantification compared to those derived from incomplete cardiac MR datasets. For instance, the results obtained when compensating for the absence of two basal slices show that the mean differences to the reference of stroke volume and ejection fraction are only -1.3 mL and -1.0%, respectively, which are significantly smaller than those calculated from the incomplete data (-26.8 mL and -6.7%). The proposed approach can improve the reliability of high-throughput image analysis in large-scale population studies, minimising the need for re-scanning patients or discarding incomplete acquisitions. 0852c4b9a8
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