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Optimal transport regularization for implicit neural representations of cell shape sequences
Martens, T.H. (2025) Optimal transport regularization for implicit neural representations of cell shape sequences.
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| Abstract: | The study of living-cell sequences at the microscopic level is critical for advancing our understanding of cell dynamics and interactions. This knowledge is particularly valuable in fields such as disease monitoring and drug development. Despite its importance, high-quality 4D living-cell data remains sparse. Implicit neural representations (INRs) offer a promising solution by enhancing the temporal resolution of the cell shape sequences. However, they often suffer from overfitting to training data. This thesis investigates the application of optimal transport (OT)-based regularization to enhance 3D time-lapse INRs. We propose two regularization methods: one minimizes the Sinkhorn distance between consecutive time points, and the other guides the INR using OT-based interpolations at intermediate time points. Experiments on synthetic datasets demonstrate that both methods improve temporal interpolation, reduce output variability, and enhance visual coherence, particularly in modeling bifurcating structures. Nevertheless, there remains room for improvement, especially if the data contains abrupt deformations and high-frequency motion. Additionally, in terms of latent space generalization, the proposed methods did not yield significant benefits. Overall, this work highlights the potential of OT-based methods to improve temporal consistency in 3D time-lapse INRs, especially in structured or moderately complex settings. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 31 mathematics |
| Programme: | Applied Mathematics MSc (60348) |
| Link to this item: | https://purl.utwente.nl/essays/107203 |
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