Automated 2D, 2.5D, and 3D Segmentation of Coral Reef Pointclouds and Orthoprojections.

Enabled by advancing technology, coral reef researchers increasingly prefer use of image-based surveys over approaches depending solely upon in situ observations, interpretations, and recordings of divers. The images collected, and derivative products such as orthographic projections and 3D models, allow researchers to study a comprehensive digital twin of their field sites. Spatio-temporally located twins can be compared and annotated, enabling researchers to virtually return to sites long after they have left them. While these new data expand the variety and specificity of biological investigation that can be pursued, they have introduced the much-discussed Big Data Problem: research labs lack the human and computational resources required to process and analyze imagery at the rate it can be collected. The rapid development of unmanned underwater vehicles suggests researchers will soon have access to an even greater volume of imagery and other sensor measurements than can be collected by diver-piloted platforms, further exacerbating data handling limitations. Thoroughly segmenting (tracing the extent of and taxonomically identifying) organisms enables researchers to extract the information image products contain, but is very time-consuming. Analytic techniques driven by neural networks offer the possibility that the segmentation process can be greatly accelerated through automation. In this study, we examine the efficacy of automated segmentation on three different image-derived data products: 3D models, and 2D and 2.5D orthographic projections thereof; we also contrast their relative accessibility and utility to different avenues of biological inquiry. The variety of network architectures and parameters tested performed similarly, ∼80% IoU for the genus Porites, suggesting that the primary limitations to an automated workflow are 1) the current capabilities of neural network technology, and 2) consistency and quality control in image product collection and human training/testing dataset generation.

Considering the rates of growth in two taxa of coral across Pacific islands.

Reef-building coral taxa demonstrate considerable flexibility and diversity in reproduction and growth mechanisms. Corals take advantage of this flexibility to increase or decrease size through clonal expansion and loss of live tissue area (i.e. via reproduction and mortality of constituent polyps). The biological lability of reef-building corals may be expected to map onto varying patterns of demography across environmental contexts which can contribute to geographic variation in population dynamics. Here we explore the patterns of growth of two common coral taxa, corymbose Pocillopora and massive Porites, across seven islands in the central and south Pacific. The islands span a natural gradient of environmental conditions, including a range of pelagic primary production, a metric linked to the relative availability of inorganic nutrients and heterotrophic resources for mixotrophic corals, and sea surface temperature and thermal histories. Over a multi-year sampling interval, most coral colonies experienced positive growth (greater planar area of live tissue in second relative to first time point), though the distributions of growth varied across islands. Island-level median growth did not relate simply to estimated pelagic primary productivity or temperature. However, at locations that experienced an extreme warm-water event during the sampling interval, most Porites colonies experienced net losses of live tissue and nearly all Pocillopora colonies experienced complete mortality. While descriptive statistics of demographics offer valuable insights into trends and variability in colony change through time, simplified models predicting growth patterns based on summarized oceanographic metrics appear inadequate for robust demographic prediction. We propose that the complexity of life history strategies among colonial reef-building corals introduces unique demographic flexibility for colonies to respond to a wide breadth of environmental conditions.

Visualizing whole-brain DTI tractography with GPU-based Tuboids and LoD management.

Diffusion Tensor Imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large-collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.