Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
This work addresses a specific problem in connectomics for researchers needing accurate neuron reconstructions from anisotropic data, representing an incremental improvement over existing methods.
The paper tackles the challenge of automatic dense neural reconstruction from anisotropic EM data by presenting a segmentation method that agglomerates a 3D over-segmentation based on 3D affinity predictions using a 3D U-net trained with the MALIS approach, demonstrating strength and robustness in experiments on multiple datasets.
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.