Segmentation deep learning ALgorithm based on MONai toolbox: single and multi-label segmentation software developed by QIMP team-Vienna.

Brain Tissue Segmentation on IBSR18 Dataset

nnUNet paddlepaddle version

Project for CV, SJTU SE

This is the regular nnUNet but with three new features: 1 - Training with cyclic learning rate, producing checkpoints from different convergent minima. 2 - an ensemble of the different checkpoints is used to determine uncertainty of each fold. 3 - On inference prediction is made using the lowest uncertainty prediction from 5 folds.

Brain tissue segmentation using several algorithms: EM, Atlas-based methods, nnunet

nnUNet benchmarks for The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma (UCSF-ALPTDG) dataset.

This is a repository hosting all models detailed in the article Brain tumour segmentation with incomplete imaging data.

A repository that allows users to apply the DLICV method to their brain imaging data.

Code for implementing and comparing the Generalized Surface Loss to other loss functions

Brain tissue (WM, GM, CSF) segmentation using both multi-atlas and nnUNet approaches. This project was developed for a course titled "Medical Image Segmentation and Applications" - MISA under MAIA master program.

The segmentation in this project is conducted through the nnUNet framework, followed by the extraction of pituitary tumor features using the radiomics package. The final step involves designing the classifier using the scikit-learn package, resulting in an achieved classification accuracy of approximately 91%.

Official repository for "Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation"

nnUNet benchmarks for The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset.

Playing with SimpleITK and nnU-Net to process data from the CHAOS challenge on Google Colab.

3D Slicer nnUNet integration to streamline usage for nnUNet based AI extensions.

3D Slicer extension for fully-automatic segmentation of CT and CBCT dental volumes.

MTMAUNet: Multi-Task Multi-axis Attention UNet

Utilities to run nnUNet more easily using containers

Automated Segmentation for Eustachian Tube Disfunction