Non-Rigid Registration of Time-Lapse Cell Microscopy Images Improved by a Semi-Incremental Optimization Method

Non-rigid registration of cell nuclei in time-lapse microscopy images needs accurate estimation of the deformation fields between the reference and all other images. To address the issue of accumulated errors in classical temporal incremental registration approaches, we introduce a new semi-incremental optimization method. Based on a proper initialization constructed through motion concatenation, which exploits temporal coherence within the image sequence, the deformation field between each image and the reference is computed, reducing accumulated errors and yielding more reliable results. Experiments on real time-lapse cell images demonstrate that our method outperforms previous approaches, including deep learning models, in terms of registration accuracy. Additionally, computation on a GPU significantly enhances efficiency.