Fast and Accurate Electron Microscopy Image Registration with 3D Convolution

We propose an unsupervised deep learning method for serial electron microscopy (EM) image registration with fast speed and high accuracy. Current registration methods are time consuming in practice due to the iterative optimization procedure. We model the registration process as a parametric function in the form of convolutional neural networks, and optimize its parameters based on features extracted from training serial EM images in a training set. Given a new series of EM images, the deformation field of each serial image can be rapidly generated through the learned function. Specifically, we adopt a spatial transformer layer to reconstruct features in the subject image from the reference ones while constraining smoothness on the deformation field. Moreover, for the first time, we introduce the 3D convolution layer to learn the relationship between several adjacent images, which effectively reduces error accumulation in serial EM image registration. Experiments on two popular EM datasets, Cremi and FIB25, demonstrate our method can operate in an unprecedented speed while providing competitive registration accuracy compared with state-of-the-art methods, including learning-based ones.