Electron Microscopy

71 papers with code • 0 benchmarks • 0 datasets

Analysis of data from various types of electron microscopes.

Datasets


Greatest papers with code

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

MrGiovanni/UNetPlusPlus 11 Dec 2019

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).

 Ranked #1 on Medical Image Segmentation on EM (IoU metric)

Computed Tomography (CT) Electron Microscopy +3

Flood-Filling Networks

google/ffn 1 Nov 2016

State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments.

Boundary Detection Electron Microscopy +1

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

AngeLouCN/CFPNet-Medicine 10 May 2021

By comparison, CFPNet-M achieves comparable segmentation results on all five medical datasets with only 0. 65 million parameters, which is about 2% of U-Net, and 8. 8 MB memory.

Electron Microscopy Medical Image Segmentation +1

Reconstructing continuous distributions of 3D protein structure from cryo-EM images

zhonge/cryodrgn ICLR 2020

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution.

3D Volumetric Reconstruction Cryogenic Electron Microscopy (cryo-EM) +1

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

GunhoChoi/FusionNet-Pytorch 16 Dec 2016

Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.

Brain Image Segmentation Electron Microscopy +2

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

neuropoly/axondeepseg 11 Jul 2019

In this paper we provide a framework for Deep Active Learning applied to a real-world scenario.

Active Learning Electron Microscopy +1

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

neuropoly/axondeepseg 3 Nov 2017

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness.

Electron Microscopy

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

tbepler/topaz 22 Mar 2018

Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination.

Electron Microscopy

NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale

zudi-lin/pytorch_connectomics 13 Jul 2021

Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages.

Electron Microscopy Instance Segmentation +2

Machine learning of hierarchical clustering to segment 2D and 3D images

janelia-flyem/gala 25 Mar 2013

We aim to improve segmentation through the use of machine learning tools during region agglomeration.

Active Learning Electron Microscopy