Electron Microscopy Image Segmentation
9 papers with code • 3 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Electron Microscopy Image Segmentation
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3D Platelet EM
uBench
Most implemented papers
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
lee-gihun/mediar
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Cell segmentation is a fundamental task for computational biology analysis.
Large-Scale Electron Microscopy Image Segmentation in Spark
In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine.
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning.
UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images
urakubo/UNI-EM
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bioRxiv Neuroscience 2019
The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation.
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge.
Dense cellular segmentation for EM using 2D–3D neural network ensembles
Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets.
Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
jovialniyo93/cell-segmentation
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Journal of Computational Biology 2024
To address these issues, we propose a novel framework called RA-SE-ASPP-Net, which incorporates Residual Blocks, Attention Mechanism, Squeeze-and-Excitation connection, and Atrous Spatial Pyramid Pooling to achieve precise and robust cell segmentation.
