MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.

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

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

MIST: A simple, scalable, and end-to-end framework for 3D medical imaging segmentation.

cardiAc ultrasound Segmentation & Color-dopplEr dealiasiNg Toolbox (ASCENT)

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

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

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

nnUNet paddlepaddle version

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

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

Brain Tissue Segmentation on IBSR18 Dataset

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

Utilities to run nnUNet more easily using containers

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

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.

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery

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

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%.

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.