A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.

Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets.

Size control of the inner ear via hydraulic feedback.

Unlabelled: Animals make organs of precise size, shape, and symmetry but how developing embryos do this is largely unknown. Here, we combine quantitative imaging, physical theory, and physiological measurement of hydrostatic pressure and fluid transport in zebrafish to study size control of the developing inner ear. We find that fluid accumulation creates hydrostatic pressure in the lumen leading to stress in the epithelium and expansion of the otic vesicle.

Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution.

Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire brain.

Lamellar projections in the endolymphatic sac act as a relief valve to regulate inner ear pressure.

The inner ear is a fluid-filled closed-epithelial structure whose function requires maintenance of an internal hydrostatic pressure and fluid composition. The endolymphatic sac (ES) is a dead-end epithelial tube connected to the inner ear whose function is unclear. ES defects can cause distended ear tissue, a pathology often seen in hearing and balance disorders.

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.

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms.

True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo.

Recovery of shape and size in a developing organ pair.

Background: Paired organs in animals are largely bilaterally symmetric despite inherent noise in most biological processes. How is precise organ shape and size achieved during development despite this noise? Examining paired organ development is a challenge because it requires repeated quantification of two structures in parallel within living embryos. Here we combine bilateral quantification of morphology through time with asymmetric perturbations to study regulation of organ shape, size, and symmetry in developing organ pairs.

Membrane dynamics of dividing cells imaged by lattice light-sheet microscopy.

Membrane remodeling is an essential part of transferring components to and from the cell surface and membrane-bound organelles and for changes in cell shape, which are particularly critical during cell division. Earlier analyses, based on classical optical live-cell imaging and mostly restricted by technical necessity to the attached bottom surface, showed persistent formation of endocytic clathrin pits and vesicles during mitosis. Taking advantage of the resolution, speed, and noninvasive illumination of the newly developed lattice light-sheet fluorescence microscope, we reexamined their assembly dynamics over the entire cell surface and found that clathrin pits form at a lower rate during late mitosis.

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.

Interplay of cell shape and division orientation promotes robust morphogenesis of developing epithelia.

Epithelial cells acquire functionally important shapes (e.g., squamous, cuboidal, columnar) during development.

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.

Specified neural progenitors sort to form sharp domains after noisy Shh signaling.

Sharply delineated domains of cell types arise in developing tissues under instruction of inductive signal (morphogen) gradients, which specify distinct cell fates at different signal levels. The translation of a morphogen gradient into discrete spatial domains relies on precise signal responses at stable cell positions. However, cells in developing tissues undergoing morphogenesis and proliferation often experience complex movements, which may affect their morphogen exposure, specification, and positioning.

ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes.

The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes.

Streaming level set algorithm for 3D segmentation of confocal microscopy images.

We present a high performance variant of the popular geodesic active contours which are used for splitting cell clusters in microscopy images. Previously, we implemented a linear pipelined version that incorporates as many cues as possible into developing a suitable level-set speed function so that an evolving contour exactly segments a cell/nuclei blob. We use image gradients, distance maps, multiple channel information and a shape model to drive the evolution.

Detection of spatially correlated objects in 3D images using appearance models and coupled active contours.

We consider the problem of segmenting 3D images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. Our approach involves an initial modeling phase followed by a data-fitting segmentation phase. In the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates.

Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging.

In neurobiology, the 3D reconstruction of neurons followed by the identification of dendritic spines is essential for studying neuronal morphology, function and biophysical properties. Most existing methods suffer from problems of low reliability, poor accuracy and require much user interaction. In this paper, we present a method to reconstruct dendrites using a surface representation of the neuron.

Tensor classification of N-point correlation function features for histology tissue segmentation.

In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure.

An imaging workflow for characterizing phenotypical change in large histological mouse model datasets.

Motivation: This paper presents a workflow designed to quantitatively characterize the 3D structural attributes of macroscopic tissue specimens acquired at a micron level resolution using light microscopy. The specific application is a study of the morphological change in a mouse placenta induced by knocking out the retinoblastoma gene.

Result: This workflow includes four major components: (i) serial section image acquisition, (ii) image preprocessing, (iii) image analysis involving 2D pair-wise registration, 2D segmentation and 3D reconstruction, and (iv) visualization and quantification of phenotyping parameters.

Reconstruction of cellular biological structures from optical microscopy data.

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane.

Detection and visualization of surface-pockets to enable phenotyping studies.

In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface.

Preventing signal degradation during elastic matching of noisy DCE-MR eye images.

Motion during the acquisition of dynamic contrast enhanced MRI can cause model-fitting errors requiring co-registration. Clinical implementations use a pharmacokinetic model to determine lesion parameters from the contrast passage. The input to the model is the time-intensity plot from a region of interest (ROI) covering the lesion extent.

Rb is critical in a mammalian tissue stem cell population.

The inactivation of the retinoblastoma (Rb) tumor suppressor gene in mice results in ectopic proliferation, apoptosis, and impaired differentiation in extraembryonic, neural, and erythroid lineages, culminating in fetal death by embryonic day 15.5 (E15.5).