BEAS-Net: a Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2D Echocardiography.

Left ventricle (LV) segmentation of 2D echocardiography images is an essential step in the analysis of cardiac morphology and function and - more generally - diagnosis of cardiovascular diseases. Several deep learning (DL) algorithms have recently been proposed for the automatic segmentation of the LV, showing significant performance improvement over the traditional segmentation algorithms. However, unlike the traditional methods, prior information about the segmentation problem, e.g. anatomical shape information, is not usually incorporated for training the DL algorithms. This can degrade the generalization performance of the DL models on unseen images if their characteristics are somewhat different from those of the training images, e.g. low-quality testing images. In this study, a new shape-constrained deep convolutional neural network (CNN) - called BEAS-Net - is introduced for automatic LV segmentation. The BEAS-Net learns how to associate the image features, encoded by its convolutional layers, with anatomical shape-prior information derived by the B-spline explicit active surface (BEAS) algorithm to generate physiologically meaningful segmentation contours when dealing with artifactual or low-quality images. The performance of the proposed network was evaluated using three different in-vivo datasets and was compared a deep segmentation algorithm based on the U-Net model. Both networks yielded comparable results when tested on images of acceptable quality, but the BEAS-Net outperformed the benchmark DL model on artifactual and low-quality images.

STERN: Attention-driven Spatial Transformer Network for abnormality detection in chest X-ray images.

Chest X-ray scans are frequently requested to detect the presence of abnormalities, due to their low-cost and non-invasive nature. The interpretation of these images can be automated to prioritize more urgent exams through deep learning models, but the presence of image artifacts, e.g. lettering, often generates a harmful bias in the classifiers and an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise, in which an image is either normal or abnormal, using an attention-driven and spatially unsupervised Spatial Transformer Network (STERN), that takes advantage of a novel domain-specific loss to better frame the region of interest. Unlike the state of the art, in which this type of networks is usually employed for image alignment, this work proposes a spatial transformer module that is used specifically for attention, as an alternative to the standard object detection models that typically precede the classifier to crop out the region of interest. In sum, the proposed end-to-end architecture dynamically scales and aligns the input images to maximize the classifier's performance, by selecting the thorax with translation and non-isotropic scaling transformations, and thus eliminating artifacts. Additionally, this paper provides an extensive and objective analysis of the selected regions of interest, by proposing a set of mathematical evaluation metrics. The results indicate that the STERN achieves similar results to using YOLO-cropped images, with reduced computational cost and without the need for localization labels. More specifically, the system is able to distinguish abnormal frontal images from the CheXpert dataset, with a mean AUC of 85.67% - a 2.55% improvement vs. the 0.98% improvement achieved by the YOLO-based counterpart in comparison to a standard baseline classifier. At the same time, the STERN approach requires less than 2/3 of the training parameters, while increasing the inference time per batch in less than 2 ms. Code available via GitHub.

Automatic Contrast Generation from Contrastless Computed Tomography.

The use of contrast-enhanced computed tomography (CTCA) for detection of coronary artery disease (CAD) exposes patients to the risks of iodine contrast-agents and excessive radiation, increases scanning time and healthcare costs. Deep learning generative models have the potential to artificially create a pseudo-enhanced image from non-contrast computed tomography (CT) scans.In this work, two specific models of generative adversarial networks (GANs) - the Pix2Pix-GAN and the Cycle-GAN - were tested with paired non-contrasted CT and CTCA scans from a private and public dataset. Furthermore, an exploratory analysis of the trade-off of using 2D and 3D inputs and architectures was performed. Using only the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR), it could be concluded that the Pix2Pix-GAN using 2D data reached better results with 0.492 SSIM and 16.375 dB PSNR. However, visual analysis of the output shows significant blur in the generated images, which is not the case for the Cycle-GAN models. This behavior can be captured by the evaluation of the Fréchet Inception Distance (FID), that represents a fundamental performance metric that is usually not considered by related works in the literature.Clinical relevance- Contrast-enhanced computed tomography is the first line imaging modality to detect CAD resulting in unnecessary exposition to the risk of iodine contrast and radiation in particularly in young patients with no disease. This algorithm has the potential of being translated into clinical practice as a screening method for CAD in asymptomatic subjects or quick rule-out method of CAD in the acute setting or centres with no CTCA service. This strategy can eventually represent a reduction in the need for CTCA reducing its burden and associated costs.

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping.

Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

Assisted probe guidance in cardiac ultrasound: A review.

Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

An Incidental Finding Of A Persistent Sciatic Artery - Case Report And Literature Review.

Persistent sciatic artery is a rare anatomic variation due to the lack of regression during fetal development, associated sometimes with abnormalities of the iliofemoral arterial axis and predisposing the patients to aneurysm formation and thromboembolism, which can compromise the limb. In our department, we assisted a 59-year-old male with an acute limb ischemia as result of an incidental finding of a thrombosed persistent sciatic artery aneurysm.

Detection of COVID-19 in Point of Care Lung Ultrasound.

The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite.

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning.

The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.

MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging.

Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of -9 ± 16 ml, -1 ± 10 ml, -2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.

Systematic Comparison of Left Ventricular Geometry Between 3D-Echocardiography and Cardiac Magnetic Resonance Imaging.

Aims: Left ventricular (LV) volumes estimated using three-dimensional echocardiography (3D-echo) have been reported to be smaller than those measured using cardiac magnetic resonance (CMR) imaging, but the underlying causes are not well-understood. We investigated differences in regional LV anatomy derived from these modalities and related subsequent findings to image characteristics. Methods and Results: Seventy participants (18 patients and 52 healthy participants) were imaged with 3D-echo and CMR (<1 h apart). Three-dimensional left ventricular models were constructed at end-diastole (ED) and end-systole (ES) from both modalities using previously validated software, enabling the fusion of CMR with 3D-echo by rigid registration. Regional differences were evaluated as mean surface distances for each of the 17 American Heart Association segments, and by comparing contours superimposed on images from each modality. In comparison to CMR-derived models, 3D-echo models underestimated LV end-diastolic volume (EDV) by -16 ± 22, -1 ± 25, and -18 ± 24 ml across three independent analysis methods. Average surface distance errors were largest in the basal-anterolateral segment (11-15 mm) and smallest in the mid-inferoseptal segment (6 mm). Larger errors were associated with signal dropout in anterior regions and the appearance of trabeculae at the lateral wall. Conclusions: Fusion of CMR and 3D-echo provides insight into the causes of volume underestimation by 3D-echo. Systematic signal dropout and differences in appearances of trabeculae lead to discrepancies in the delineation of LV geometry at anterior and lateral regions. A better understanding of error sources across modalities may improve correlation of clinical indices between 3D-echo and CMR.

Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking.

Ca2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus' boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca2+ signal. The analysis package represents a significant improvement to available Ca2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca2+ signal extraction workflows.

LNDb challenge on automatic lung cancer patient management.

Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen's kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen's kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions - data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision-making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient follow-up recommendation.

Shear Wave Elastography Using High-Frame-Rate Imaging in the Follow-Up of Heart Transplantation Recipients.

OBJECTIVES: The purpose of this study was to investigate whether propagation velocities of naturally occurring shear waves (SWs) at mitral valve closure (MVC) increase with the degree of diffuse myocardial injury (DMI) and with invasively determined LV filling pressures as a reflection of an increase in myocardial stiffness in heart transplantation (HTx) recipients. BACKGROUND: After orthotopic HTx, allografts undergo DMI that contributes to functional impairment, especially to increased passive myocardial stiffness, which is an important pathophysiological determinant of left ventricular (LV) diastolic dysfunction. Echocardiographic SW elastography is an emerging approach for measuring myocardial stiffness in vivo. Natural SWs occur after mechanical excitation of the myocardium, for example, after MVC, and their propagation velocity is directly related to myocardial stiffness, thus providing an opportunity to assess myocardial stiffness at end-diastole. METHODS: A total of 52 HTx recipients who underwent right heart catheterization (all) and cardiac magnetic resonance (CMR) (n = 23) during their annual check-up were prospectively enrolled. Echocardiographic SW elastography was performed in parasternal long axis views of the LV using an experimental scanner at 1,135 ± 270 frames per second. The degree of DMI was quantified with T1 mapping. RESULTS: SW velocity at MVC correlated best with native myocardial T1 values (r = 0.75; p < 0.0001) and was the best noninvasive parameter that correlated with pulmonary capillary wedge pressures (PCWP) (r = 0.54; p < 0.001). Standard echocardiographic parameters of LV diastolic function correlated poorly with both native T1 and PCWP values. CONCLUSIONS: End-diastolic SW propagation velocities, as measure of myocardial stiffness, showed a good correlation with CMR-defined diffuse myocardial injury and with invasively determined LV filling pressures in patients with HTx. Thus, these findings suggest that SW elastography has the potential to become a valuable noninvasive method for the assessment of diastolic myocardial properties in HTx recipients.

A Novel 2-D Speckle Tracking Method for High-Frame-Rate Echocardiography.

Speckle tracking echocardiography (STE) is a clinical tool to noninvasively assess regional myocardial function through the quantification of regional motion and deformation. Even if the time resolution of STE can be improved by high-frame-rate (HFR) imaging, dedicated HFR STE algorithms have to be developed to detect very small interframe motions. Therefore, in this article, we propose a novel 2-D STE method, purposely developed for HFR echocardiography. The 2-D motion estimator consists of a two-step algorithm based on the 1-D cross correlations to separately estimate the axial and lateral displacements. The method was first optimized and validated on simulated data giving an accuracy of ~3.3% and ~10.5% for the axial and lateral estimates, respectively. Then, it was preliminarily tested in vivo on ten healthy volunteers showing its clinical applicability and feasibility. Moreover, the extracted clinical markers were in the same range as those reported in the literature. Also, the estimated peak global longitudinal strain was compared with that measured with a clinical scanner showing good correlation and negligible differences (-20.94% versus -20.31%, p -value = 0.44). In conclusion, a novel algorithm for STE was developed: the radio frequency (RF) signals were preferred for the axial motion estimation, while envelope data were preferred for the lateral motion. Furthermore, using 2-D kernels, even for 1-D cross correlation, makes the method less sensitive to noise.

Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists' Screening Performance.

Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, searching lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer-aided detection systems can be used as second observers, they may bias radiologists and introduce significant time overheads. With this in mind, this study assesses the potential of using gaze information for integrating automatic detection systems in the clinical practice. For that purpose, 4 radiologists were asked to annotate 20 scans from a public dataset while being monitored by an eye tracker device, and an automatic lung nodule detection system was developed. Our results show that radiologists follow a similar search routine and tend to have lower fixation periods in regions where finding errors occur. The overall detection sensitivity of the specialists was 0.67±0.07, whereas the system achieved 0.69. Combining the annotations of one radiologist with the automatic system significantly improves the detection performance to similar levels of two annotators. Filtering automatic detection candidates only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.

Interplay of cardiac remodelling and myocardial stiffness in hypertensive heart disease: a shear wave imaging study using high-frame rate echocardiography.

AIMS: To determine myocardial stiffness by means of measuring the velocity of naturally occurring myocardial shear waves (SWs) at mitral valve closure (MVC) and investigate their changes with myocardial remodelling in patients with hypertensive heart disease. METHODS AND RESULTS: Thirty-three treated arterial hypertension (HT) patients with hypertrophic left ventricular (LV) remodelling (59 ± 14 years, 55% male) and 26 aged matched healthy controls (55±15 years, 77% male) were included. HT patients were further divided into a concentric remodelling (HT1) group (13 patients) and a concentric hypertrophy (HT2) group (20 patients). LV parasternal long-axis views were acquired with an experimental ultrasound scanner at 1266 ± 317 frames per seconds. The SW velocity induced by MVC was measured from myocardial acceleration maps. SW velocities differed significantly between HT patients and controls (5.83 ± 1.20 m/s vs. 4.04 ± 0.96 m/s; P < 0.001). In addition, the HT2 group had the highest SW velocities (P < 0.001), whereas values between controls and the HT1 group were comparable (P = 0.075). Significant positive correlations were found between SW velocity and LV remodelling (interventricular septum thickness: r = 0.786, P < 0.001; LV mass index: r = 0.761, P < 0.001). SW velocity normalized for wall stress indicated that myocardial stiffness in the HT2 group was twice as high as in controls (P < 0.001), whereas values of the HT1 group overlapped with the controls (P = 1.00). CONCLUSIONS: SW velocity as measure of myocardial stiffness is higher in HT patients compared with healthy controls, particularly in advanced hypertensive heart disease. Patients with concentric remodelling have still normal myocardial properties whereas patients with concentric hypertrophy show significant stiffening.

Non-invasive myocardial performance mapping using 3D echocardiographic stress-strain loops.

Regional contribution to left ventricular (LV) ejection is of much clinical importance but its assessment is notably challenging. While deformation imaging is often used, this does not take into account loading conditions. Recently, a method for intraventricular pressure estimation was proposed, thus allowing for loading conditions to be taken into account in a non-invasive way. In this work, a method for 3D automatic myocardial performance mapping in echocardiography is proposed by performing 3D myocardial segmentation and tracking, thus giving access to local geometry and strain. This is then used to assess local LV stress-strain relationships which can be seen as a measure of local myocardial work. The proposed method was validated against 18F-fluorodeoxyglucose positron emission tomography, the reference method to clinically assess local metabolism. Averaged over all patients, the mean correlation between FDG-PET and the proposed method was [Formula: see text]. In conclusion, stress-strain loops were, for the first time, estimated from 3D echocardiography and correlated to the clinical gold standard for local metabolism, showing the future potential of real-time 3D echocardiography (RT3DE) for the assessment of local metabolic activity of the heart.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Velocities of Naturally Occurring Myocardial Shear Waves Increase With Age and in Cardiac Amyloidosis.

OBJECTIVES: This study sought to evaluate whether velocity of naturally occurring myocardial shear waves (SW) could relate to myocardial stiffness (MS) in vivo. BACKGROUND: Cardiac SW imaging has been proposed as a noninvasive tool to assess MS. SWs occur after mechanical excitation of the myocardium (e.g., mitral valve closure [MVC] and aortic valve closure [AVC]), and their propagation velocity is theoretically related to MS, thus providing an opportunity to assess stiffness at end-diastole (ED) and end-systole. However, given that SW propagation in vivo is complex, it remains unclear whether natural SW velocity effectively relates to MS. METHODS: This study prospectively enrolled 50 healthy volunteers (HV) (43.7 ± 17.1 years of age) and 18 patients with cardiac amyloidosis (CA) (68.0 ± 9.8 years of age). HV were divided into 3 age groups: group I, 20 to 39 years of age (n = 24); group II, 40 to 59 years of age (n = 11); and group III, 60 to 80 years of age (n = 15). Parasternal long-axis views were acquired using an experimental scanner. Tissue (Doppler) acceleration maps were extracted from an anatomical M-mode along the midline of the left ventricular septum. RESULTS: SW propagation velocity was significantly higher in CA patients than in HV after both MVC (3.54 ± 0.93 m/s vs. 6.33 ± 1.63 m/s, respectively; p < 0.001) and AVC (3.75 ± 0.76 m/s vs. 5.63 ± 1.13 m/s, respectively; p < 0.001). Similarly, SW propagation velocity differed significantly among age groups in HV, with a significantly higher value for group III than for group I, both occurring after MVC (p < 0.001) and AVC (p < 0.01). Moreover, SW propagation velocity after MVC was found to be significantly higher in patients with an increasing grade of diastolic dysfunction (p < 0.001). Finally, positive correlation was found between SW velocities after MVC and mitral inflow-to-mitral relaxation velocity ratio (E/E') (r = 0.74; p = 0.002). CONCLUSIONS: End-diastole SW velocities were significantly higher in patients with CA, patients with a higher grade of diastolic dysfunction, and elderly volunteers. These findings thus suggest that the speed of naturally induced SWs may be related to MS.