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Wavelet-based segmentation and convex hull approaches for quantitative analysis of biological imaging data
Imaging-based analysis of developmental processes are crucial to understand the mechanisms controlling plant and animal development. In vertebrate embryos such as the zebrafish embryo, nuclei segmentation plays an important role to detect and quantify nuclei over space and time. However, limitations of the image quality and segmentation methods may affect the segmentation performance. In plant including studies on Arabidopsis epidermis growth, cellular shape change dictates organ size control and growth behavior, and quantitative image analysis of dynamics cell patterning is needed to link the cause and effect between cells and organs. Here we provide a series of new quantitative biological imaging methods a series of new quantitative biological imaging methods and tools including wavelet-based segmentation method in zebrafish embryo development studies and convex hull approach for quantitative shape analyses of lobed plant cells.
Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. In this paper we present a size dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio (SNR). The program separates overlapping nuclei, identifies cell cycle states and minimizes intensity attenuation in object identification. The wavelet-based methods presented herein achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to Zebrafish embryonic development IN TOTO quantification and developed an automatic interactive imaging analysis platform named WaveletSEG, that integrates nuclei segmentation, image registration, and nuclei shape analysis. A set of additional functions we developed include a 3D ground truth annotation tool, a synthetic image generator, a segmented training datasets export tool, and data visualization interfaces are also incorporated in WaveletSEG for additional data analysis and data validation.
In addition to our work in Zebrafish, we developed image analysis tools for quantitative studies of cell-to-organ in plants. Given the importance of the epidermis and this particular cell type for leaf expansion, there is a strong need to understand how pavement cells morph from a simple polyhedral shape into highly lobed and interdigitated cells. Currently, it is still unclear how and when patterns of lobing are initiated in pavement cells, and one major technological bottleneck to address the problem is the lack of a robust and objective methodology to identify and track lobing events during the transition from simple cell geometry to lobed cells. We develop a convex-hull-based algorithm termed LobeFinder to identify lobes, quantify geometric properties, and create a useful graphical output for further analysis. The algorithm is validated against manually curated cell images of pavement cells of widely varying sizes and shapes. The ability to objectively count and detect new lobe initiation events provides an improved quantitative framework to analyze mutant phenotypes, detect symmetry-breaking events in time-lapse image data, and quantify the time-dependent correlation between cell shape change and intracellular factors that may play a role in the morphogenesis process.