Mathematical modelling and image processing for some challenges in the In Vitro Fertilisation clinic

Ostler, Timothy 2023. Mathematical modelling and image processing for some challenges in the In Vitro Fertilisation clinic. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

In Vitro Fertilisation (IVF) is a treatment involving the fertilisation of human egg cells in the laboratory to create embryos, which are transferred to the uterus of the patient in the hope that they become pregnant. Only 19% of treatments worldwide result in a live birth. This thesis involves interdisciplinary collaboration with experimentalists and the London Women’s Clinic, to identify and tackle a series of mathematical challenges and barriers to improving success rates in IVF clinics. We first explore the use of Differential Dynamic Microscopy (DDM) as a non-invasive oocyte health assessment tool. Identifying challenges that limit the clinical usefulness of DDM, we develop a methodology, using synthetic data, to enhance and validate parameter fitting in DDM. We optimise the selection of synthetic data parameters, and present a new pipeline for generating parameter fitting. After showing existing non-linear curve fitting algorithms are inaccurate in DDM applications, we establish a new machine learning parameter fitting pipeline, trained exclusively on synthetic data and applied in real datasets. We, subsequently, explore the application of DDM to phase-contrast microscopy. Phase-contrast images exhibit shadowing, leading to anisotropy in the DDM matrix and invalidating a key assumption of DDM. We derive an analytic expression describing this anisotropy, and conclude for isotropic motions that shadowing does not affect parameter fitting. For anisotropic motion, we also outline conditions on the microscope setup and imaged behaviour that affect fitting error. The second part of this thesis considers challenges related to cryopreservation of oocytes and embryos through a rapid-cooling technique, vitrification. We numerically simulate the process of vitrification (rapid freezing) and show that cooling rates are unaffected by the number or arrangement of embryos or oocytes on the device, which validates current protocol. Additionally, the challenge of predicting embryo viability from time-lapse images of post-thaw embryo re-expansion is tackled. We implement machine-learning image segmentation to measure the cross-sectional area of the embryo, and identify non-linear re-expansion as a new 5 metric indicating lower implantation rates by as much as 3% in a sample of clinical data. 6

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Mathematics
Date of First Compliant Deposit:	16 April 2024
Last Modified:	16 Apr 2024 14:40
URI:	https://orca.cardiff.ac.uk/id/eprint/167981

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