pytorch-3dunet

PyTorch implementation of 3D U-Net and its variants: - UNet3D: Standard 3D U-Net based on 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation - ResidualUNet3D: Residual 3D U-Net based on Superhuman Accuracy on the SNEMI3D Connectomics Challenge - ResidualUNetSE3D: Similar to ResidualUNet3D with the addition of Squeeze and Excitation blocks based on Deep Learning Semantic Segmentation for High- Resolution Medical Volumes. Original squeeze and excite paper: Squeeze-and- Excitation Networks The code allows for training the U-Net for both: semantic segmentation (binary and multi-class) and regression problems (e.g. de-noising, learning deconvolutions).

homepage: https://github.com/wolny/pytorch-3dunet

version	versionsuffix	toolchain
1.6.0	-CUDA-11.7.0	foss/2022a

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