LANMC: LSTM-Assisted Non-Rigid Motion Correction on FPGA for Calcium Image Stabilization

Calcium imaging is an emerging technique for visualizing and recording neural population activity at large scale in vivo. Non-rigid motion correction is a critical step in the calcium image analysis pipeline due to non-uniform deformations of the brain tissue during the data collection. Existing non-rigid motion correction algorithms are costly in computation time and energy, and it is hard to implement such algorithm in real time on an embedded device. In this paper, we propose LANMC, an LSTM-assisted non-rigid motion correction method for real-time calcium image stabilization. This method reduces the computational cost by using the LSTM inference to predict the non-rigid motion. Based on this method, we demonstrate a non-rigid motion correction implementation for real-time calcium image stabilization on FPGA. Experimental results show that the non-rigid motion correction can be accomplished within 80 mu s on the Ultra96 under 300 MHz frequency, and the latency outperforms that on a 12-thread CPU by 82x.