ArticleIEEE transactions on medical imaging2021
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells.Nature neuroscience · 2026Article
- SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unsupervised segmentation of dynamic pulmonary MRI using cross-modality adaptation with annotated CT images.International journal of computer assisted radiology and surgery · 2026Article
- Connectome of a human foveal retina.bioRxiv : the preprint server for biology · 2026Article
- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
- Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context.bioRxiv : the preprint server for biology · 2025Article
- SKOOTS: Skeleton-oriented object segmentation for mitochondria.bioRxiv : the preprint server for biology · 2025Article
- Article
- RoboEM: automated 3D flight tracing for synaptic-resolution connectomics.Nature methods · 2024Article
- RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations.Frontiers in neuroinformatics · 2022Article
- Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.IEEE transactions on medical imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A "metric graph" on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network. Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex "self-contact" motifs. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.