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MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images
TLDR
The MitoEM dataset is introduced, a 3D mitochondria instance segmentation dataset with two (30μm)3 volumes from human and rat cortices respectively, 3, 600× larger than previous benchmarks. Expand
Synapse-Aware Skeleton Generation for Neural Circuits
TLDR
This work proposes a synapse-aware skeleton generation strategy to transform the reconstructed volumes into an information-rich yet abstract format on which neuroscientists can perform biological analysis and run simulations and demonstrates the results on three large-scale connectomic datasets. Expand
VICE: Visual Identification and Correction of Neural Circuit Errors
TLDR
This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity‐related errors, with automated likely‐error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. Expand
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
TLDR
The AxonEM dataset is introduced, which consists of two 30×30×30 μm EM image volumes from the human and mouse cortex, respectively, to provide dense 3D axon instance segmentation, enabling largescale evaluation of axon reconstruction methods. Expand
A Topological Nomenclature for 3D Shape Analysis in Connectomics
TLDR
A novel topological nomenclature system to name objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures by converting the volumetric representation into the topology-preserving reduced graph to untangle the objects. Expand
Two Stream Active Query Suggestion for Active Learning in Connectomics
TLDR
An end-to-end active learning framework with the query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics, and an unsupervised one optimized on all raw images to capture diverse image features, which can later be improved by fine-tuning on new labels. Expand
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
TLDR
A novel hybrid-representation learning model is proposed that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks and significantly outperforms state-of-the-art nuclei segmentation approaches. Expand