Abstract
This paper focuses on image registration using the automatic differentiation of deep learning frameworks. Specifically, a method for the registration of image sequences is proposed and tested on retinal video ophthalmoscopic data and brain DCE MR images. PyTorch auto-differentiation has been used as a core of an optimisation tool to find the optimal image transformation parameters. It allows us to easily design a loss function for our registration tasks. The image registration was achieved by simultaneous registration of all images using a global loss function without the need of the reference frame.
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Acknowledgements
This work is supported by the Czech Science Foundation project no. 18-24089S. Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the programme Projects of Large Research, Development, and Innovation Infrastructures. The authors also acknowledge the contribution of St. Anne’s University Hospital in Brno, which provided MRI data.
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Vicar, T., Jakubicek, R., Chmelik, J., Kolar, R. (2023). Registration of Medical Image Sequences Using Auto-differentiation. In: Su, R., Zhang, Y., Liu, H., F Frangi, A. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2022. Lecture Notes in Electrical Engineering, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-16-6775-6_15
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