rachitsaluja/UCSF-BMSR-benchmarks
nnUNet benchmarks for The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset.
This project offers pre-trained models for automatically identifying brain metastases in MRI scans. It takes various sequences of brain MRI images (like T1-post, T1-pre, FLAIR) as input and outputs segmented images highlighting the metastatic lesions. This is designed for medical researchers and neuroradiologists working with brain tumor imaging data.
No commits in the last 6 months.
Use this if you need to benchmark or apply state-of-the-art deep learning models for segmenting brain metastases from MRI scans, especially if working with the UCSF-BMSR dataset.
Not ideal if you are looking for a commercial solution or a ready-to-use clinical tool, as this project is intended for research and non-commercial use.
Stars
10
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 27, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rachitsaluja/UCSF-BMSR-benchmarks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
dipy/dipy
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for...
Project-MONAI/MONAI
AI Toolkit for Healthcare Imaging
Project-MONAI/MONAILabel
MONAI Label is an intelligent open source image labeling and learning tool.
neuronets/nobrainer
A framework for developing neural network models for 3D image processing.
axondeepseg/axondeepseg
Axon/Myelin segmentation using Deep Learning