Volume electron microscopy (vEM) is the most advanced and scalable technique for reconstructing synaptic-level wiring diagrams of the nervous system. Following image acquisition, the first critical step is reconstruction of a digitized volume, which assembles millions of electron microscope images into a coherent 3D volume that will underpin all downstream analyses. Existing methods work best with artifact-free datasets or rely on computationally intensive deep learning approaches or time-consuming human editing, restricting the broader applicability of vEM. To circumvent these challenges, we have developed FEABAS, a scalable, cross-platform, open-source software package designed to elastically montage and align electron microscope image datasets with high efficiency and precision. It leverages adaptive mesh modeling and finite element methods, enabling robust handling of datasets containing common artifacts such as wrinkles, folds, tears, and broken sections, while maintaining a lightweight, accessible implementation suitable for diverse computational environments.
Wu, Y., Lichtman, J. W.
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