Automatic segmentation of 2D echocardiography ultrasound images by means of generative adversarial network.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression.

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

A two-dimensional angular interpolation based on radial basis functions for high frame rate ultrafast imaging.

To solve the problem of reduced image quality in plane wave imaging (PWI), coherent plane wave compounding (CPWC) has been introduced, based on a combination of plane wave images from several directions (i.e., with different angles). However, the number of angles needed to reach a reasonable image quality affects the maximum achievable frame rate in CPWC. In this study, we suggest reducing the tradeoff between the image quality and the frame rate in CPWC by employing two-dimensional (2D) interpolation based on radial basis functions. More specifically, we propose constructing a three-dimensional spatio-angular structure to integrate both spatial and angular information into the reconstruction prior to 2D interpolation. The rationale behind our proposal is to reduce the number of transmissions and then apply the 2D interpolation along the angle dimension to reconstruct the missing information corresponding to the angles not selected for CPWC imaging. To evaluate the proposed technique, we applied it to the PWI challenges in the medical ultrasound database. Results show that we can achieve 3× to 4× improvement in frame rate while maintaining acceptable image quality compared to the case of using all the angles.

The emergence of division of labour through decentralized social sanctioning.

Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specialization and division of labour. Unlike most other animals, humans learn by trial and error during their lives what role to take on. However, when some critical roles are more attractive than others, and individuals are self-interested, then there is a social dilemma: each individual would prefer others take on the critical but unremunerative roles so they may remain free to take one that pays better. But disaster occurs if all act thus and a critical role goes unfilled. In such situations learning an optimum role distribution may not be possible. Consequently, a fundamental question is: how can division of labour emerge in groups of self-interested lifetime-learning individuals? Here, we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labour involving all critical roles. Such social norms work by redistributing rewards within the population to disincentivize antisocial roles while incentivizing prosocial roles that do not intrinsically pay as well as others.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Increasing frame rate of echocardiography based on a novel 2D spatio-temporal meshless interpolation.

BACKGROUND: Increasing temporal resolution through numerical methods aids clinicians to evaluate fast moving structures of the heart with more confidence. METHODOLOGY: In this study, a spatio-temporal numerical method is proposed to increase the frame rate based on two-dimensional (2D) interpolation. More specifically, we propose a novel intensity variation time surface (IVTS) strategy to incorporate both temporal and spatial information in the reconstruction. In this regard, we exploit radial basis functions (RBFs) for 2D interpolation. The reason for choosing RBFs for this task is manifold. First, RBFs are able to interpolate on large-scale datasets. Moreover, their mathematical implementation is simple. Another important property of this interpolation technique, which is addressed in this study, is its meshless nature. The meshless property enables higher up-sampling (UpS) rates for echocardiography to improve temporal resolution without noticeably degrading image quality. To evaluate the proposed approach, we tested the RBF interpolation on 2D/3D echocardiography datasets. The reconstructed frames were analyzed using different image quality metrics, and the results were compared with two popular techniques from the literature. RESULTS: The findings demonstrated that, with a down-sampling rate of 3, the proposed technique outperformed the best existing method by 42%, 87%, 8%, and 11%, respectively, in terms of mean square error (MSE), contrast to noise ratio (CNR), peak signal-to-noise ratio (PSNR), and figure of merit (FOM). It should be noted that the proposed method is comparable to the best available method in terms of structural similarity (SSIM) index. Furthermore, when compared to the original images, the results of employing our technique on radio-frequency (RF) level analysis demonstrated that the reconstruction accuracy is satisfactory in terms of image quality criterion. CONCLUSION: Finally, it is worthwhile noting that the proposed method is better than (or comparable to) the other methods in terms of reconstruction performance and processing time. Therefore, the RBF interpolation can be a promising alternative to the existing methods.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

High Frame Rate Ultrasound Imaging by Means of Tensor Completion: Application to Echocardiography.

High frame rate ultrasound (US) imaging enables the monitoring of fast-moving organs. In echocardiography, this is especially needed due to the existence of rapidly moving structures, such as the heart valves. In the last two decades, various methods have been proposed to improve the frame rate. Here, we propose a novel method, based on binary coding patterns (BCPs) and tensor completion (TC), to increase the temporal resolution (i.e., frame rate) in the preprocessing stage of conventional focused ultrasound imaging (CFUI). The rationale behind our proposal is to perform, at first, the beamforming of a fraction of the scan lines, randomly selected in each frame based on BCP. Then, we reconstruct the missing scan lines through TC. The latter is an effective technique for recovering missing information from a low-rank tensor, based on a small number of observations using rank minimization. Following our approach, reducing the transmissions events needed to generate an image, the frame rate is increased by the same proportion. We have applied the proposed technique to a pre-beamformed radio frequency (RF) echocardiographic dataset. Our results show that we can improve the frame rate by a factor from 3 to 4, while keeping the structural similarity (SSIM) of the reconstructed tensor and the original one at values higher than 0.98.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions.

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.

Multi-strategy coevolving aging particle optimization.

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.