Non-rigid registration of temporal live cell microscopy image sequences using deep learning

Wenzhe Yin; Vadim O. Chagin; M. Cristina Cardoso; Karl Rohr

doi:10.1117/12.2611440

4 April 2022 Non-rigid registration of temporal live cell microscopy image sequences using deep learning

Wenzhe Yin, Vadim O. Chagin, M. Cristina Cardoso, Karl Rohr

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120321B (2022) https://doi.org/10.1117/12.2611440
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Registration of microscopy images is an important task in biomedical applications. We introduce a deep learning approach for non-rigid registration of cell nuclei in temporal microscopy image sequences. First, we present a segmentation-based registration method which combines different transformation models and can cope with strong image intensity changes. Second, as an extension, we propose a joint segmentation and registration method which includes a cycle consistency loss and automatically determines the segmentation. Both methods do not need labeled data for network training. The methods were applied to live cell microscopy images of cell nuclei and yield better results than baseline methods.

Conference Presentation

Citation Download Citation

Wenzhe Yin, Vadim O. Chagin, M. Cristina Cardoso, and Karl Rohr "Non-rigid registration of temporal live cell microscopy image sequences using deep learning", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321B (4 April 2022); https://doi.org/10.1117/12.2611440

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