Deep manifold learning of cell morphology and motion
Organization:
Funded by: | EEMCS Faculty
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PhD: |
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Supervisor: | chair MIA:
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Description:
Information about the characteristics of cells is important. For instance, understanding the shape properties of a cell over time, may give insight into the distinction between good and bad cells. However, cell populations, such as populations of sperm cells and protoplasts, are often analyzed by looking at average values of a population. Only considering average quantities is not ideal as a cell population is not homogeneous; it consists of different individual cells with different responses to stimuli. As a consequence, it is beneficial to study the properties of single cells.
Microfluidic technologies are a perfect tool to analyze the properties of individual cells. Using such technologies, videos can be obtained of the moving cells. These images contain a plethora of information about the cells. This project is aimed at using Geometric Deep Learning to generate more knowledge about the heterogeneity of cells in a population. More precisely, a latent encoding is created that encodes an informative, understandable, and discriminable feature representation of cells. The challenge is to extract informative cell morphology like shape, structure, form or size, and informative cell dynamics like velocity, acceleration, rotation, deformation or grows from dynamic imaging data and combine it into a unified higher-order feature representation. The resulting Deep Learning based manifolds can then be explored visually (explainable AI) and used for important classification questions like: Which is the optimal sperm cell to choose? What is a perfectly growing protoplast?
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Publications:
Jump to: 2024 | 2023 | 2022
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (2024)SIAM journal on imaging sciences, 17(4), 2302 - 2330. Dummer, S., Brune, C. & Strisciuglio, N.https://doi.org/10.1137/24M1644730Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging (2024)[Working paper › Preprint]. ArXiv.org. Garzia, S., Rygiel, P., Dummer, S., Cademartiri, F., Celi, S. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2407.20728Rda-Inr: Riemannian Diffeomorphic Autoencoding Via Implicit Neural Representations (2024)[Contribution to conference › Poster] SIAM Conference on Imaging Science, IS 2024. Dummer, S., Strisciuglio, N. & Brune, C.Generative modeling of living cells with SO(3)-equivariant implicit neural representations (2024)Medical image analysis, 91. Article 102991. Wiesner, D., Suk, J., Dummer, S., Nečasová, T., Ulman, V., Svoboda, D. & Wolterink, J. M.https://doi.org/10.1016/j.media.2023.102991 Discovering efficient periodic behaviors in mechanical systems via neural approximators (2023)Optimal Control Applications and Methods, 44(6), 3052-3079. Wotte, Y. P., Dummer, S., Botteghi, N., Brune, C., Stramigioli, S. & Califano, F.https://doi.org/10.1002/oca.3025Riemannian Shape Manifold Learning with Applications to Biological Data (2023)[Contribution to conference › Poster] EEMCS Research Networking Day 2023. Dummer, S., Strisciuglio, N. & Brune, C.Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space (2023)[Working paper › Preprint]. ArXiv.org. Mazilu, I., Wang, S., Dummer, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2307.15461Rda-inr: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (2023)[Working paper › Preprint]. ArXiv.org. Dummer, S., Strisciuglio, N. & Brune, C.https://doi.org/10.48550/arXiv.2305.12854Generative modeling of living cells with SO(3)-equivariant implicit neural representations (2023)[Working paper › Preprint]. ArXiv.org. Wiesner, D., Suk, J., Dummer, S., Nečasová, T., Ulman, V., Svoboda, D. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2304.08960Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space (2023)In Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Limassol, Cyprus, September 25–28, 2023, Proceedings (pp. 201-211) (Lecture Notes in Computer Science; Vol. 14185). Springer. Mazilu, I., Wang, S., Dummer, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.1007/978-3-031-44240-7_20 Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators (2022)[Working paper › Preprint]. ArXiv.org. Wotte, Y., Dummer, S., Botteghi, N., Brune, C., Stramigioli, S. & Califano, F.https://doi.org/10.48550/arXiv.2212.14253Structure preserving implicit shape encoding via flow regularization (2022)[Contribution to conference › Abstract] Geometric Deep Learning in Medical Image Analysis, GeoMedIA 2022. Dummer, S., Strisciuglio, N. & Brune, C.https://openreview.net/pdf?id=YcjlgyX_Ur1Implicit Neural Representations for Generative Modeling of Living Cell Shapes (2022)[Working paper › Preprint]. ArXiv.org. Wiesner, D., Suk, J., Dummer, S., Svoboda, D. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2207.06283Implicit Neural Representations for Generative Modeling of Living Cell Shapes (2022)In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV (pp. 58-67) (Lecture Notes in Computer Science; Vol. 13434). Springer. Wiesner, D., Suk, J., Dummer, S., Svoboda, D. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-16440-8_6 Pictures:
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