Dense cellular segmentation for EM using 2D-3D neural network ensembles.

ABSTRACT: Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells an

SUBMITTER: Guay MD 

PROVIDER: S-EPMC7844272 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature