ArticlePloS one2013
Machine learning of hierarchical clustering to segment 2D and 3D images.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The trial behind it
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Who cites it
33 citing papers in PubMed.
- Connectome quality converges predictably to reveal optimal stopping points during proofreading.bioRxiv : the preprint server for biology · 2026Article
- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
- Normalized Raman Imaging for Studies of Tissue Physiology of the Kidney.bioRxiv : the preprint server for biology · 2025Article
- Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos.The journal of physical chemistry. B · 2024Article
- Improving the Neural Segmentation of Blurry Serial SEM Images by Blind Deblurring.Computational intelligence and neuroscience · 2023Article
- Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.Frontiers in neuroinformatics · 2023Article
- Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.Cells & development · 2022Article
- Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes.BMC bioinformatics · 2022Article
- SimCP-NS: similarity-based copy paste for semi-supervised 3D EM neuron segmentation.Bioinformatics (Oxford, England) · 2022Article
- Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.IEEE transactions on medical imaging · 2021Article
- DeepACSON automated segmentation of white matter in 3D electron microscopy.Communications biology · 2021Article
- Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.GigaScience · 2020Article
- A convolutional neural network segments yeast microscopy images with high accuracy.Nature communications · 2020Article
- MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Generative and discriminative model-based approaches to microscopic image restoration and segmentation.Microscopy (Oxford, England) · 2020Review
- Learning cellular morphology with neural networks.Nature communications · 2019Article
- Automated 3D Axonal Morphometry of White Matter.Scientific reports · 2019Article
- Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy.Current opinion in neurobiology · 2019Review
- Article
- Analyzing Image Segmentation for Connectomics.Frontiers in neural circuits · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.
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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.