71 papers with code • 0 benchmarks • 0 datasets
Analysis of data from various types of electron microscopes.
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)
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.
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.
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution.
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
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.
Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination.
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.
We aim to improve segmentation through the use of machine learning tools during region agglomeration.