An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs

In the last decade, level-set methods have been actively developed for applications in image registration, segmentation, tracking, and reconstruction. However, the development of a wide variety of level-set PDEs and their numerical discretization schemes, coupled with hybrid combinations of PDE terms, stopping criteria, and reinitialization strategies, has created a software logistics problem. In the absence of an integrative design, current toolkits support only specific types of level-set implementations which restrict future algorithm development since extensions require significant code duplication and effort. In the new NIH/NLM Insight Toolkit (ITK) v4 architecture, we implemented a level-set software design that is flexible to different numerical (continuous, discrete, and sparse) and grid representations (point, mesh, and image-based). Given that a generic PDE is a summation of different terms, we used a set of linked containers to which level-set terms can be added or deleted at any point in the evolution process. This container-based approach allows the user to explore and customize terms in the level-set equation at compile-time in a flexible manner. The framework is optimized so that repeated computations of common intensity functions (e.g. gradient and Hessians) across multiple terms is eliminated. The framework further enables the evolution of multiple level-sets for multi-object segmentation and processing of large datasets. For doing so, we restrict level-set domains to subsets of the image domain and use multithreading strategies to process groups of subdomains or level-set functions. Users can also select from a variety of reinitialization policies and stopping criteria. Finally, we developed a visualization framework that shows the evolution of a level-set in real-time to help guide algorithm development and parameter optimization. We demonstrate the power of our new framework using confocal microscopy images of cells in a developing zebrafish embryo

ABSTRACT Generalized Clustering Methods for Multivariate Data

Efficient analysis (including segmentation and classification) of multivariate data is an inherently complex task in which features occur as salient members of clusters in a multi-dimensional data space. The clusters assume a variety of distributions and frequently overlap, leading to difficulty in segmentation and classification. In many cases, similar but distinct features in the dataset are grouped into the same cluster in global classification. Additionally, partial voluming effects from the acquisition process complicate the reconstruction of accurate spatial features. In this paper, we present a novel framework for the analysis of multivariate datasets. We employ simple linear material models under partial voluming assumption, and recursively classify data samples, thus obtaining both global and regional segmentation in feature space. The simplicity of our approach makes it computationally more efficient compared to other methods for classification of multivariate data. We present results that employ data from light microscopy scanners and the Visible Human repository

Improved Long-Term Imaging of Embryos with Genetically Encoded α-Bungarotoxin

Rapid advances in microscopy and genetic labeling strategies have created new opportunities for time-lapse imaging of embryonic development. However, methods for immobilizing embryos for long periods while maintaining normal development have changed little. In zebrafish, current immobilization techniques rely on the anesthetic tricaine. Unfortunately, prolonged tricaine treatment at concentrations high enough to immobilize the embryo produces undesirable side effects on development. We evaluate three alternative immobilization strategies: combinatorial soaking in tricaine and isoeugenol, injection of α-bungarotoxin protein, and injection of α-bungarotoxin mRNA. We find evidence for co-operation between tricaine and isoeugenol to give immobility with improved health. However, even in combination these anesthetics negatively affect long-term development. α-bungarotoxin is a small protein from snake venom that irreversibly binds and inactivates acetylcholine receptors. We find that α-bungarotoxin either as purified protein from snakes or endogenously expressed in zebrafish from a codon-optimized synthetic gene can immobilize embryos for extended periods of time with few health effects or developmental delays. Using α-bungarotoxin mRNA injection we obtain complete movies of zebrafish embryogenesis from the 1-cell stage to 3 days post fertilization, with normal health and no twitching. These results demonstrate that endogenously expressed α-bungarotoxin provides unprecedented immobility and health for time-lapse microscopy

Total.lsm

This is a xyzt confocal image set in the Zeiss lsm format. It is the raw data used in Figure 3C

Data from: Feedback between tissue packing and neurogenesis in the zebrafish neural tube

Balancing the rate of differentiation and proliferation in developing tissues is essential to produce organs of robust size and composition. Although many molecular regulators have been established, how these connect to physical and geometrical aspects of tissue architecture is poorly understood. Here, using high-resolution timelapse imaging, we find that changes to cell geometry associated with dense tissue packing play a significant role in regulating differentiation rate in the zebrafish neural tube. Specifically, progenitors that are displaced away from the apical surface due to crowding, tend to differentiate in a Notch-dependent manner. Using simulations we show that interplay between progenitor density, cell shape and changes in differentiation rate could naturally result in negative-feedback control on progenitor cell number. Given these results, we suggest a model whereby differentiation rate is regulated by density dependent effects on cell geometry to: (1) correct variability in cell number; and (2) balance the rates of proliferation and differentiation over development to ‘fill’ the available space

Tricaine and isoeugenol co-operate towards healthier immobilization.

<p><b>(A)</b> Heat map of percent immobile for 48 combinations of tricaine (0–200 μg/ml) and isoeugenol (0–0.003% v/v). Embryos were dechorionated and soaked from 24–27 hpf when they were assayed for immobility. <b>(B)</b> Continuation of treatments from (A), embryos were assayed for immobility at 72 hpf. <b>(C,D)</b> Representative micrographs of control (C) and 200 μg/ml tricaine treated (D) embryos. Arrow in (D) shows failure of semicircular canal projection fusion. Asterisk in (D) shows pericardial edema. <b>(E)</b> Heat map of percent of embryos with pericardial edema at 72 hpf. <b>(F)</b> Percent control otic vesicle diameter (OVD) was calculated by dividing the average of 10–30 experimental embryos by the average of 10–30 control embryos. Heat map of percent control OVD for the combinatorial treatments. OVD was measured at 72 hpf using micrographs like those in (C, D). Percentage is based on normalization to untreated control. <b>(G)</b> Merge of heatmaps from (A, 27 hpf) and (F) that highlights the tradeoffs between embryo immobility and healthy development.</p

α-bungarotoxin immobilizes embryos while permitting normal development.

<p><b>(A)</b> Percent of embryos immobile after injection of α-bungarotoxin protein (0.046–4.6ng) into the yolk at 24 hpf. <b>(B)</b> Percent of embryos immobile after injection of α-bungarotoxin mRNA (20–400 pg) into the yolk at 24 hpf. <b>(C)</b> Percent of embryos immobile after injection of of α-bungarotoxin mRNA (5–100 pg) into the 1-cell zygote. <b>(D)</b> Percent control OVD at 72 hpf for injection of α-bungarotoxin mRNA into the 1-cell zygote (green), into the yolk (yellow), and reference anesthetic treatments that permitted long-term immobilization (blue). (*) Not significantly different from control, Mann-Whitney-Wilcoxon two tailed P-value 0.87. (†) Significantly different from control, Mann-Whitney-Wilcoxon two tailed P-value 0.0011. <b>(E, G)</b> Control embryo at 72 hpf that was injected with 50 pg of membrane-citrine mRNA into the 1-cell zygote. <b>(F, H)</b> 72 hpf embryo that was injected with 50 pg of α-bungarotoxin mRNA into the 1-cell zygote. <b>(I)</b> Control larva at 8 days post fertilization (dpf) injected with 50 pg of membrane-citrine mRNA into the 1-cell zygote. <b>(J)</b> 8 dpf larva that was injected with 50 pg of α-bungarotoxin mRNA into the 1-cell zygote.</p

Algorithm-enabled quantification of cell dynamics during somite formation.

<p>Retrospective cell tracing of epithelial (yellow) and mesenchymal (red) cells from formed somites at (B) 5ss back to the presomitic mesoderm at (A) 3ss. (C) Corresponding decrease in somite tissue surface area during the formation of somites 3, 4, and 5. (D) Epithelial and mesenchymal cell numbers in respective somites at 5ss. (E,F) Three-dimensional cell shape quantified by the length of their principal axes at 3ss and 5ss. (G,H) Scatter plots of elongation () and cell volumes at 3ss and 5ss. The two cell populations show different behavior. Statistical analysis of the two distributions show that mesenchymal cells (red) tend to cluster, round-up, and shrink in size on average.</p