Segmentation of Biomedical Images is based on U-Net. This U-Net implementation using Keras and TensorFlow has varying depth that can be specified by model input.
Biomedical Image Segmentation using Unet with PyTorch
Generate Synthetic Shapes in 3D for Biomedical Image Augmentation and Synthesis.
How To Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum Intensity Projections and Diffusion Models
The repo of the ANN's class final project in NCU (Toruń, Poland). It is an implementation of the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation".
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