<p>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.</p>
<p> </p>
<p>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. </p>
<p> </p>
<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/10255793 |
| Date | 14 January 2021 |
| Creators | Tzu-Ching Wu (7819853) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Wavelet-based_segmentation_and_convex_hull_approaches_for_quantitative_analysis_of_biological_imaging_data/10255793 |
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