Dmitry Fedorov

Bisque: a platform for bioimage analysis and management

MOTIVATION Advances in the field of microscopy have brought about the need for better image management and analysis solutions. Novel imaging techniques have created vast stores of images and metadata that are difficult to organize, search, process and analyze. These tasks are further complicated by conflicting and proprietary image and metadata formats, that impede analyzing and sharing of images and any associated data. These obstacles have resulted in research resources being locked away in digital media and file cabinets. Current image management systems do not address the pressing needs of researchers who must quantify image data on a regular basis. RESULTS We present Bisque, a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend Bisque with both data model and analysis extensions in order to adapt the system to local needs. Bisques extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel bioimage analyses. Several case studies using Bisque with specific applications are presented as an indication of how users can expect to extend Bisque for their own purposes.

Silencing of CDK5 Reduces Neurofibrillary Tangles in Transgenic Alzheimer's Mice

Alzheimers disease is a major cause of dementia for which treatments remain unsatisfactory. Cyclin-dependent kinase 5 (CDK5) is a relevant kinase that has been hypothesized to contribute to the tau pathology. Several classes of chemical inhibitors for CDK5 have been developed, but they generally lack the specificity to distinguish among various ATP-dependent kinases. Therefore, the efficacy of these compounds when tested in animal models cannot definitively be attributed to an effect on CDK5. However, RNA interference (RNAi) targeting of CDK5 is specific and can be used to validate CDK5 as a possible treatment target. We delivered a CDK5 RNAi by lentiviral or adenoassociated viral vectors and analyzed the results in vitro and in vivo. Silencing of CDK5 reduces the phosphorylation of tau in primary neuronal cultures and in the brain of wild-type C57BL/6 mice. Furthermore, the knockdown of CDK5 strongly decreased the number of neurofibrillary tangles in the hippocampi of triple-transgenic mice (3×Tg-AD mice). Our data suggest that this downregulation may be attributable to the reduction of the CDK5 availability in the tissue, without affecting the CDK5 kinase activity. In summary, our findings validate CDK5 as a reasonable therapeutic target for ameliorating tau pathology.

Structure-Forming Corals and Sponges and Their Use as Fish Habitat in Bering Sea Submarine Canyons

Continental margins are dynamic, heterogeneous settings that can include canyons, seamounts, and banks. Two of the largest canyons in the world, Zhemchug and Pribilof, cut into the edge of the continental shelf in the southeastern Bering Sea. Here currents and upwelling interact to produce a highly productive area, termed the Green Belt, that supports an abundance of fishes and squids as well as birds and marine mammals. We show that in some areas the floor of these canyons harbors high densities of gorgonian and pennatulacean corals and sponges, likely due to enhanced surface productivity, benthic currents and seafloor topography. Rockfishes, including the commercially important Pacific ocean perch, Sebastes alutus, were associated with corals and sponges as well as with isolated boulders. Sculpins, poachers and pleuronectid flounders were also associated with corals in Pribilof Canyon, where corals were most abundant. Fishes likely use corals and sponges as sources of vertical relief, which may harbor prey as well as provide shelter from predators. Boulders may be equivalent habitat in this regard, but are sparse in the canyons, strongly suggesting that biogenic structure is important fish habitat. Evidence of disturbance to the benthos from fishing activities was observed in these remote canyons. Bottom trawling and other benthic fishing gear has been shown to damage corals and sponges that may be very slow to recover from such disturbance. Regulation of these destructive practices is key to conservation of benthic habitats in these canyons and the ecosystem services they provide.

Multi-Focus Imaging using Local Focus Estimation and Mosaicking

We propose an algorithm to generate one multi-focus image from a set of images acquired at different focus settings. First images are registered to avoid large misalignments. Each image is tiled with overlapping neighborhoods. Then, for each region the tile that corresponds to the best focus is chosen to construct the multi-focus image. The overlapping tiles are then seamlessly mosaicked. Our approach is presented for images from optical microscopes and hand held consumer cameras, and demonstrates robustness to temporal changes and small misalignments. The implementation is computationally efficient and gives good results.

Towards automated bioimage analysis: from features to semantics

Recent advances in bio-molecular imaging have afforded biologists a more thorough understanding of cellular functions in complex tissue structures. For example, high resolution fluorescence images of the retina reveal details about tissue restructuring during detachment experiments. Time sequence imagery of microtubules provides insight into subcellular dynamics in response to cancer treatment drugs. However, technological progress is accompanied by a rapid proliferation of image data. Traditional analysis methods, namely manual measurements and qualitative assessments, become time consuming and are often nonreproducible. Computer vision tools can efficiently analyze these vast amounts of data with promising results. This paper provides an overview of several challenges faced in bioimage processing and our recent progress in addressing these issues

Simultaneous cell tracking and image alignment in 3D CLSM imagery of growing Arabidopsis thaliana sepals

In this research we propose a combined cell matching and image alignment method for tracking cells based on their nuclear locations in 3D fluorescent Confocal Laser Scanning Microscopy (CLSM) image sequences. We then apply it to study the cell division pattern in the developing sepal of the small plant Arabidopsis thaliana. The method is based on geometric hashing and inherits its invariance to rotation, translation and scale. The method consists of three steps. In the first step the centroids of nuclei are detected using a previously developed cell detection algorithm, reducing the CLSM volumes to 3D point clouds, wherein every point represents a nuclear centroid with an associated confidence level. In the second step centroids between images are matched in two phases. First geometric hashing is used to find an initial set of centroid matches, then using the initial matches a dense matching is obtained through a novel iterative point matching algorithm. In the last step centroid matches are used to estimate transformations and register all input images to a common frame. Our algorithm has successfully aligned 12 volumes encompassing 72 hours data set and matched 258 nuclear lifelines.

Scalable image informatics

Abstract Images and video play a major role in scientific discoveries. Significant new advances in imaging science over the past two decades have resulted in new devices and technologies that are able to probe the world at nanoscales to planetary scales. These instruments generate massive amounts of multimodal imaging data. In addition to the raw imaging data, these instruments capture additional critical information—the metadata—that include the imaging context. Further, the experimental conditions are often added manually to such metadata that describe processes that are not implicit in the instrumentation metadata. Despite these technological advances in imaging sciences, resources for curation, distribution, sharing, and analysis of such data at scale are still lacking. Robust image analysis workflows have the potential to transform image-based sciences such as biology, ecology, remote sensing, materials science, and medical imaging. In this context, this chapter presents BisQue, a novel eco-system where scientific image analysis methods can be discovered, tested, verified, refined, and shared among users on a shared, cloud-based infrastructure. The vision of BisQue is to enable large-scale, data-driven scientific explorations. The following sections will discuss the core requirements of such an architecture, challenges in developing and deploying the methods, and will conclude with an application to image recognition using deep learning.