ArticleNature methods2023
Local shape descriptors for neuron segmentation.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
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- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
- SmartEM: machine learning-guided electron microscopy.Nature methods · 2026Article
- MambaSSM: efficient segmentation of brain structures in anisotropic 3D EM images via state-space models.Frontiers in neuroscience · 2026Article
- Uncertainty quantification for connectomics.Nature methods · 2025Article
- A workflow for semi-automated volume correlative light microscopy and transmission electron tomography.Journal of microscopy · 2025Article
- Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context.bioRxiv : the preprint server for biology · 2025Article
- CAVE: Connectome Annotation Versioning Engine.Nature methods · 2025Article
- Segment Anything for Microscopy.Nature methods · 2025Article
- Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation.Research square · 2024Article
- Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation.bioRxiv : the preprint server for biology · 2024Article
- Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.PNAS nexus · 2024Article
- Next-generation AI for connectomics.Nature methods · 2024Article
- Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells.bioRxiv : the preprint server for biology · 2024Article
- Article
- Building an automated three-dimensional flight agent for neural network reconstruction.Nature methods · 2024Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
- RoboEM: automated 3D flight tracing for synaptic-resolution connectomics.Nature methods · 2024Article
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Abstract
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
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