Seasonality of Marine Litter Hotspots in the Wider Caribbean Region

The persistent increase in marine plastic litter has become a major global concern, with one of the highest plastic concentrations in the world’s oceans found in the Wider Caribbean Region (WCR). In this study, we use marine plastic litter tracking simulations to investigate where marine plastic accumulates, i.e., hotspots, in the WCR and how the accumulation varies on seasonal timescales. We show that most of the marine plastic waste converges on the coastlines shortly after being released into the WCR because of the strong surface current and the predominant easterly winds. Major plastic accumulations take place along (i) the western coastline of the WCR, especially the north–south-oriented coasts of Costa Rica/Nicaragua, Guatemala/Belize/Mexico, and Texas, and (ii) the coastlines of Haiti–Dominican Republic and Venezuela. Relatively low plastic accumulation is found along western Florida, the western Yucatán peninsula, and the leeward and windward Caribbean islands. Accumulation along the western WCR coastlines is modulated primarily by ocean currents and exhibits significant seasonal variabilities due to changes in wind patterns. The accumulation observed on the Haiti–Dominican Republic and Venezuela coastlines is primarily due to the proximity of large, mismanaged plastic waste sources. Finally, we discuss the uncertainty associated with the choices made in defining the different criteria for plastic beaching in the models

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations

Workflow.

<p>This figure illustrates the workflow from image selection, through coordinate reconstructions, landmark-configuration alignment, and, finally, volume estimation.</p

Distance measurements of the reconstructed pyramid.

<p>In the columns, length (side length1 to 8) and height (height1–4) measurements of the individual planes of the pyramid (1–5) are given. Actual distances were 30, 25, 20, 15, and 10 cm for lengths, and 1.8 cm for heights. Average error percentages of measurements are given and range from 0.0324 to 0.9499% for length measurements and from 6.7848 to 3.8761% for height values.</p

Identifying homologous points.

<p>The six homologous landmarks on the perineal swelling of a female Barbary macaque. In addition to the primary landmarks used for transformation parameter estimation, a number of secondary landmarks were also digitized for use in volume estimation. These might include scar or wrinkle termini or even dirt or trash affixed to the surface of the swelling. The only requirement of these landmarks is that they are visible and positionally stable in multiple views of the individual at the time the images are recorded.</p

Point-clouds of oriented landmark data and 3D-reconstructions of Barbary macaque anogenital swellings.

<p>On the left side, landmark data (green dots represent the homologous points, grey dots show secondary points) and 3D-reconstructions for female A, B, and C in an unswollen state are depicted. The right side shows landmark data and 3D-reconstructions of swollen swelling states.</p

Means and variances of the bootstrapped volumes per model type and landmarks added.

<p>a) The mean volume was affected by the type of model (large, middle, or small) as well as by the percentage of landmarks added. With an increasing number of landmarks the mean volume asymptotically increased. The blue lines indicate the values of the mean volumes of the five replications with 100% of the landmarks for each artificial swelling size. b) Variances in volume results were influenced by the type of model and the number of landmarks. As the percentage of landmarks added increased, the variance in volume results decreased.</p

Reconstructions of the models.

<p>a) 3D reconstruction of the middle size artificial swelling from two perspectives. Green dots represent the original landmarks and red dot is the landmark with the lowest z-value used to compute the bottom plane (in blue). The rectangular prisms are shown in purple and the smoothed surface of the model as a colored mesh. b) 3D reconstructions of three artificial swelling models (large, middle and small) and respective volume results.</p

Post hoc pair-wise comparisons of the GEE for volume variances and percentage of landmarks.

<p>The lower diagonal shows the absolute values of the mean differences over the standard deviation (in parentheses) and the upper diagonal shows the lower and upper confidence limits (95% CI). All significance values were sequential Bonferroni corrected and were p<0.002.</p