Person: Mosaliganti, Kishore R.
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Mosaliganti
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Kishore R.
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Mosaliganti, Kishore R.
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Publication An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs(Frontiers Media S.A., 2013) Mosaliganti, Kishore R.; Gelas, Arnaud; Megason, SeanIn 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.Publication Improved Long-Term Imaging of Embryos with Genetically Encoded α-Bungarotoxin(Public Library of Science, 2015) Swinburne, Ian; Mosaliganti, Kishore R.; Green, Amelia A.; Megason, SeanRapid 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.Publication ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes(Public Library of Science, 2012) Mosaliganti, Kishore R.; Noche, Ramil; Xiong, Fengzhu; Swinburne, Ian; Megason, SeanThe 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. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).