Petascale pipeline for precise alignment of images from serial section electron microscopy.

ABSTRACT: The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into bloc

SUBMITTER: Popovych S 

PROVIDER: S-EPMC10767115 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature