Electron Microscopy Image Segmentation
8 papers with code • 3 benchmarks • 1 datasets
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine.
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
The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation.
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.
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.