Performance evaluation of computer-aided automated master frame selection techniques for fetal echocardiography

PURPOSE

Fetal echocardiography is widely used for the assessment of fetal heart development and detection of congenital heart disease (CHD). Preliminary examination of the fetal heart involves the four-chamber view which indicates the presence of all the four chambers and its structural symmetry. Examination of various cardiac parameters is generally done using the clinically selected diastole frame. This largely depends on the expertise of the sonographer and is prone to intra- and interobservational errors. To overcome this, automated frame selection technique is proposed for the recognition of fetal cardiac chamber from fetal echocardiography.

METHODS

Three techniques have been proposed in this research study to automate the process of determining the frame referred as "Master Frame" that can be used for the measurement of the cardiac parameters. The first method uses frame similarity measures (FSM) for the determination of the master frame from the given cine loop ultrasonic sequences. FSM makes use of similarity measures such as correlation, structural similarity index (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE) to identify the cardiac cycle, and all the frames in one cardiac cycle are superimposed to form the master frame. The final master frame is obtained by considering the average of the master frame obtained using each similarity measure. The second method uses averaging of ± 20% from the midframes (AMF). The third method uses averaging of all the frames (AAF) of the cine loop sequence. Both diastole and master frames have been annotated by the clinical experts, and their ground truths are compared for validation. No segmentation techniques have been used to avoid the variability of the performance of various segmentation techniques. All the proposed schemes were evaluated using six fidelity metrics such as Dice coefficient, Jaccard ratio, Hausdorff distance, structural similarity index, mean absolute error, and Pratt figure of merit.

RESULTS

The three proposed techniques were tested on the frames extracted from 95 ultrasound cine loop sequences between 19 and 32 weeks of gestation. The feasibility of the techniques was determined by the computation of fidelity metrics between the master frame derived and the diastole frame chosen by the clinical experts. The FSM-based identified master frame found to closely match with manually chosen diastole frame and also ensures statistically significant. The method also detects automatically the cardiac cycle. The resultant master frame obtained through AMF though found to be identical to that of the diastole frame, the size of the chambers found to be reduced that can lead to inaccurate chamber measurement. The master frame obtained through AAF was not found to be identical to that of clinical diastole frame.

CONCLUSION

It can be concluded that the frame similarity measure (FSM)-based master frame can be introduced in the clinical routine for segmentation followed by cardiac chamber measurements. Such automated master frame selection also overcomes the manual intervention of earlier reported techniques in the literature. The fidelity metrics assessment further confirms the suitability of proposed master frame for automated fetal chamber recognition.

A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Reliability of the fMRI-based assessment of self-evaluation in individuals with internet gaming disorder

The self-concept-defined as the cognitive representation of beliefs about oneself-determines how individuals view themselves, others, and their actions. A negative self-concept can drive gaming use and internet gaming disorder (IGD). The assessment of the neural correlates of self-evaluation gained popularity to assess the self-concept in individuals with IGD. This attempt, however, seems to critically depend on the reliability of the investigated task-fMRI brain activation. As first study to date, we assessed test-retest reliability of an fMRI self-evaluation task. Test-retest reliability of neural brain activation between two separate fMRI sessions (approximately 12 months apart) was investigated in N = 29 healthy participants and N = 11 individuals with pathological internet gaming. We computed reliability estimates for the different task contrasts (self, a familiar, and an unknown person) and the contrast (self > familiar and unknown person). Data indicated good test-retest reliability of brain activation, captured by the "self", "familiar person", and "unknown person" contrasts, in a large network of brain regions in the whole sample (N = 40) and when considering both experimental groups separately. In contrast to that, only a small set of brain regions showed moderate to good reliability, when investigating the contrasts ("self > familiar and unknown person"). The lower reliability of the contrast can be attributed to the fact that the constituting contrast conditions were highly correlated. Future research on self-evaluation should be cautioned by the findings of substantial local reliability differences across the brain and employ methods to overcome these limitations.

Test-retest reliability of neural alcohol cue-reactivity: Is there light at the end of the magnetic resonance imaging tube?

Over the last decades, the assessment of alcohol cue-reactivity gained popularity in addiction research, and efforts were undertaken to establish neural biomarkers. This attempt however depends on the reliability of cue-induced brain activation. Thus, we assessed test-retest reliability of alcohol cue-reactivity and its implications for imaging studies in addiction. We investigated test-retest reliability of alcohol cue-induced brain activation in 144 alcohol-dependent patients over 2 weeks. We computed established reliability estimates, such as intraclass correlation (ICC), Dice and Jaccard coefficients, for the three contrast conditions of interest: 'alcohol', 'neutral' and the 'alcohol versus neutral' difference contrast. We also investigated how test-retest reliability of the different contrasts affected the capacity to establishing associations with clinical data and determining effect size estimates. Whereas brain activation, indexed by the constituting contrast conditions 'alcohol' and 'neutral' separately, displayed overall moderate (ICC > 0.4) to good (ICC > 0.75) test-retest reliability in areas of the mesocorticolimbic system, the difference contrast 'alcohol versus neutral' showed poor overall reliability (ICC < 0.40), which was related to the intercorrelation between the constituting conditions. Data simulations and analyses of craving data confirmed that the low reliability of the difference contrast substantially limited the capacity to establish associations with clinical data and precisely estimate effect sizes. Future research on alcohol cue-reactivity should be cautioned by the low reliability of the common 'alcohol versus neutral' difference contrast. We propose that this limitation can be overcome by using the constituent task conditions as an individual difference measure, when intending to longitudinally monitor brain responses.

Reliability of neural food cue-reactivity in participants with obesity undergoing bariatric surgery: a 26-week longitudinal fMRI study

Obesity is highly prevalent worldwide and results in a high disease burden. The efforts to monitor and predict treatment outcome in participants with obesity using functional magnetic resonance imaging (fMRI) depends on the reliability of the investigated task-fMRI brain activation. To date, no study has investigated whole-brain reliability of neural food cue-reactivity. To close this gap, we analyzed the longitudinal reliability of an established food cue-reactivity task. Longitudinal reliability of neural food-cue-induced brain activation and subjective food craving ratings over three fMRI sessions (T0: 2 weeks before surgery, T1: 8 weeks and T2: 24 weeks after surgery) were investigated in N = 11 participants with obesity. We computed an array of established reliability estimates, including the intraclass correlation (ICC), the Dice and Jaccard coefficients and similarity of brain activation maps. The data indicated good reliability (ICC > 0.6) of subjective food craving ratings over 26 weeks and excellent reliability (ICC > 0.75) of brain activation signals for the contrast of interest (food > neutral) in the caudate, putamen, thalamus, middle cingulum, inferior, middle and superior occipital gyri, and middle and superior temporal gyri and cunei. Using similarity estimates, it was possible to re-identify individuals based on their neural activation maps (73%) with a fading degree of accuracy, when comparing fMRI sessions further apart. The results show excellent reliability of task-fMRI neural brain activation in several brain regions. Current data suggest that fMRI-based measures might indeed be suitable to monitor and predict treatment outcome in participants with obesity undergoing bariatric surgery.

Gradients in richness and turnover of a forest passerine's diet prior to breeding: A mixed model approach applied to faecal metabarcoding data

Rather little is known about the dietary richness and variation of generalist insectivorous species, including birds, due primarily to difficulties in prey identification. Using faecal metabarcoding, we provide the most comprehensive analysis of a passerine's diet to date, identifying the relative magnitudes of biogeographic, habitat and temporal trends in the richness and turnover in diet of Cyanistes caeruleus (blue tit) along a 39 site and 2° latitudinal transect in Scotland. Faecal samples were collected in 2014-2015 from adult birds roosting in nestboxes prior to nest building. DNA was extracted from 793 samples and we amplified COI and 16S minibarcodes. We identified 432 molecular operational taxonomic units that correspond to putative dietary items. Most dietary items were rare, with Lepidoptera being the most abundant and taxon-rich prey order. Here, we present a statistical approach for estimation of gradients and intersample variation in taxonomic richness and turnover using a generalised linear mixed model. We discuss the merits of this approach over existing tools and present methods for model-based estimation of repeatability, taxon richness and Jaccard indices. We found that dietary richness increases significantly as spring advances, but changes little with elevation, latitude or local tree composition. In comparison, dietary composition exhibits significant turnover along temporal and spatial gradients and among sites. Our study shows the promise of faecal metabarcoding for inferring the macroecology of food webs, but we also highlight the challenge posed by contamination and make recommendations of laboratory and statistical practices to minimise its impact on inference.

Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data

Background

A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied.

Results

We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called jaccard (https://cran.r-project.org/package=jaccard).

Conclusion

We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.

A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases

Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long-read mapping algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this article, we combine a fast approximate read mapping algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290 × faster than Burrows-Wheeler Aligner-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each ≥5 kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and >60,000 genomes.

Measuring structural similarity in large online networks

Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences.

A permutation testing framework to compare groups of brain networks

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

Estimation of the elastic parameters of human liver biomechanical models by means of medical images and evolutionary computation

This paper presents a method to computationally estimate the elastic parameters of two biomechanical models proposed for the human liver. The method is aimed at avoiding the invasive measurement of its mechanical response. The chosen models are a second order Mooney-Rivlin model and an Ogden model. A novel error function, the geometric similarity function (GSF), is formulated using similarity coefficients widely applied in the field of medical imaging (Jaccard coefficient and Hausdorff coefficient). This function is used to compare two 3D images. One of them corresponds to a reference deformation carried out over a finite element (FE) mesh of a human liver from a computer tomography image, whilst the other one corresponds to the FE simulation of that deformation in which variations in the values of the model parameters are introduced. Several search strategies, based on GSF as cost function, are developed to accurately find the elastics parameters of the models, namely: two evolutionary algorithms (scatter search and genetic algorithm) and an iterative local optimization. The results show that GSF is a very appropriate function to estimate the elastic parameters of the biomechanical models since the mean of the relative mean absolute errors committed by the three algorithms is lower than 4%.