DeepEM3D: approaching human‐level performance on 3D anisotropic EM image segmentation

91 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, background): “A second class of UDA algorithms directly minimize suitable loss functions that enforce domain alignment (Wu et al. 2018; Zhang, David, and Gong 2017; Gabourie et al. 2019; Zhang et al. 2019; Lee et al. 2019; Yang and Soatto 2020).”
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes (2017, methodology): “DeepEM3D-Net [22] has only two 3D convolution layers to integrate 3D information in the early stages and performs 2D convolution for the rest of the following layers in an FCN.”
Superhuman Accuracy on the SNEMI3D Connectomics Challenge (2017, methodology): “Test-time augmentation Test-time augmentation has been widely adopted as an effective way of improving the quality of segmentation [3, 4, 5, 9].”