Search

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training. [ABSTRACT FROM AUTHOR]

We examine the linkage in equilibrium among (I) contract design; (2) expected prepayment and default likelihoods: and (3) the pricing of mortgage contracts by focusing upon the effects of the borrower's private information at the time of contracting. We examine the implications of these perspectives upon the empirical analysis of prepayment behavior and use the framework to examine the predominance of long-term mortgage contracts in the United States. We consider examples that explore the trade-offs between fixed and adjustable rate instruments, assumable and due-on-sale loans, and contract interest rates and initial discounts (points). [ABSTRACT FROM AUTHOR]

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation. [ABSTRACT FROM AUTHOR]

The pancreatic islet is a complex micro-organ containing numerous cell types, including endocrine, immune, and endothelial cells. The communication of these systems is lost upon isolation of the islets, and therefore the pathogenesis of diabetes can only be fully understood by studying this organized, multicellular environment in vivo. We have developed several adaptable tools to create a versatile platform to interrogate β-cell function in vivo. Specifically, we developed β-cell-selective virally-encoded fluorescent protein biosensors that can be rapidly and easily introduced into any mouse. We then coupled the use of these biosensors with intravital microscopy, a powerful tool that can be used to collect cellular and subcellular data from living tissues. Together, these approaches allowed the observation of in vivo β-cell-specific ROS dynamics using the Grx1-roGFP2 biosensor and calcium signaling using the GcAMP6s biosensor. Next, we utilized abdominal imaging windows (AIW) to extend our in vivo observations beyond single-point terminal measurements to collect longitudinal physiological and biosensor data through repeated imaging of the same mice over time. This platform represents a significant advancement in our ability to study β-cell structure and signaling in vivo, and its portability for use in virtually any mouse model will enable meaningful studies of β-cell physiology in the endogenous islet niche. [ABSTRACT FROM AUTHOR]