In the field of cell biology, there is an increasing use of time-lapse data to understand cellular function. Using automated microscopes, large numbers of images can be acquired, delivering videos of cell samples over time. Analysing the images manually is extremely time consuming as there are typically thousands of individual images in any given sequence. Additionally, decisions made by those analysing the images, e.g. labelling a mitotic phase (one of a set of distinct sequential stages of cell division) can be subjective, especially around transition boundaries between phases, leading to inconsistencies in the annotation. There is therefore a need for tools which facilitate automated high-throughput analysis. In this thesis we develop systems to automatically detect, track and analyse sub-cellular structures in image sequences to address biological research needs in three areas: (i) Mitotic phase labelling, (ii) Mitotic defect detection, and (iii) Cell volume estimation. We begin by presenting a system for automated segmentation and mitotic phase labelling using temporal models. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather than over single frames, thereby capturing the distinctive behaviour over the phases. We compare and contrast three different temporal models: Dynamic Time Warping, Hidden Markov Models, and Semi Markov Models. A new loss function is proposed for the Semi Markov model to make it more robust to inconsistencies in data annotation near transition boundaries. We then present an approach for detecting subtle chromosome segregation errors in mitosis in embryonic stem cells, targeting two cases: misaligned chromosomes in a metaphase cell, and lagging chromosomes between anaphase cells. We additionally explore an unsupervised approach to detect unusual mitotic occurrences and test its applicability to detecting misaligned metaphase chromosomes. Finally, we describe a fully automated method, suited to high-throughput analysis, for estimating the volume of spherical mitotic cells based on a learned membrane classifier and a circular Hough transform. We also describe how it is being used further in biological research.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:604518 |
| Date | January 2014 |
| Creators | Ellabban, Amr |
| Contributors | Zisserman, Andrew |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:84889934-c5f0-4bd1-b208-c0845b68ff25 |
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