-
3D U-Net: learning dense volumetric segmentation from sparse annotations
3D U-Net: learning dense volumetric segmentation from sparse annotations. -
Macular Hole Segmentation
Macular hole segmentation dataset using 3D U-Net architecture -
BraTS 2020: self-ensembled, deeply-supervised 3D U-Net CNNs
Brain tumor segmentation is a critical task for patient’s disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks,... -
BraTS 2020 Validation
The BraTS 2020 validation dataset contains the same type of MR images from 125 patients, without the ground truth annotations. -
BraTS 2020
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,... -
MICCAI 2018 segmentation decathlon challenge
MRI dataset for hippocampus segmentation -
Kidney Segmentation using 3D U-Net localized with Expectation Maximization
Kidney segmentation using 3D U-Net localized with Expectation Maximization
