ArticleBioinformatics (Oxford, England)2017
DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MambaSSM: efficient segmentation of brain structures in anisotropic 3D EM images via state-space models.Frontiers in neuroscience · 2026Article
- Mitochondria structurally remodel near synapses to fuel the sustained energy demands of plasticity.bioRxiv : the preprint server for biology · 2025Article
- SKOOTS: Skeleton-oriented object segmentation for mitochondria.bioRxiv : the preprint server for biology · 2025Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- RoboEM: automated 3D flight tracing for synaptic-resolution connectomics.Nature methods · 2024Article
- mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.Medical & biological engineering & computing · 2024Review
- Machine learning for cross-scale microscopy of viruses.Cell reports methods · 2023Review
- Deep neural network automated segmentation of cellular structures in volume electron microscopy.The Journal of cell biology · 2023Article
- Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.Frontiers in bioinformatics · 2023Article
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Article
- A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials.Scientific reports · 2022Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.PLoS computational biology · 2022Review
- Identification of Bacterial Drug-Resistant Cells by the Convolutional Neural Network in Transmission Electron Microscope Images.Frontiers in microbiology · 2022Article
- SimCP-NS: similarity-based copy paste for semi-supervised 3D EM neuron segmentation.Bioinformatics (Oxford, England) · 2022Article
- Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network.Oxidative medicine and cellular longevity · 2022Article
- Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.IEEE transactions on medical imaging · 2021Article
- CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy.IEEE transactions on medical imaging · 2021Article
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3 authors.
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Abstract
motivationProgress in 3D electron microscopy (EM) imaging has greatly facilitated neuroscience research in high-throughput data acquisition. Correspondingly, high-throughput automated image analysis methods are necessary to work on par with the speed of data being produced. One such example is the need for automated EM image segmentation for neurite reconstruction. However, the efficiency and reliability of current methods are still lagging far behind human performance.
resultsHere, we propose DeepEM3D, a deep learning method for segmenting 3D anisotropic brain electron microscopy images. In this method, the deep learning model can efficiently build feature representation and incorporate sufficient multi-scale contextual information. We propose employing a combination of novel boundary map generation methods with optimized model ensembles to address the inherent challenges of segmenting anisotropic images. We evaluated our method by participating in the 3D segmentation of neurites in EM images (SNEMI3D) challenge. Our submission is ranked #1 on the current leaderboard as of Oct 15, 2016. More importantly, our result was very close to human-level performance in terms of the challenge evaluation metric: namely, a Rand error of 0.06015 versus the human value of 0.05998. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/divelab/deepem3d/. CONTACT: sji@eecs.wsu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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