Viola-Unet is the winning solution for the validation dataset in the 2022 Intracranial Hemorrhage Segmentation challenge (INSTANCE 2022). It’s a powerful AI model designed for segmenting intracranial hemorrhages (ICH) in head CT scans. We retrained the model with more data and released Vioal-Unet version 2 (viola_v2) for academic users.
- Download Viola-GUI-version(
2.6 GB
) of one single executive file. - Double-click it,
RUN anyway
, wait a bit for its initialization, and then you can use it easily. - It works on both GPU and non-GPU OS, if any issues, feel free to contact me.
- Delete it simply if you don't want to use it anymore.
violai_UI.mp4
- Download the pre-built Docker image: viola_v2.tar.gz.
- Prepare your input folder containing all CT files for test (e.g.,
/home/yourname/Desktop/input
) - Create an empty output folder (e.g.,
/home/yourname/Desktop/predict
) - Run the program with the following commands (assuming you're on Linux OS):
docker load < viola_v2.tar.gz
docker run --gpus "device=0" --name viola -e PYTHONUNBUFFERED=1 -v /home/yourname/Desktop/input:/input -v /home/yourname/Desktop/predict:/predict viola_v2:latest
The program will:
- Read each CT file (
*.nii.gz
or*.nii
in the input folder. - Use pre-trained models (ensemble of Viola_Unet and nnU-Net) to segment possible ICH from the CT scans.
- Save the segmented masks to the output folder (with exactly the same name as input file)
- Output inference messages to the terminal and save all messages to
prediction_info.csv
. Example inference message:
model nnUNet loaded successfully!
model Viola_s loaded successfully!
---------------start predicting input file: 002.nii.gz - 1/2 ----------------
Predicted lesion volume : 6.282 ml
Segmention was saved to file: 002.nii.gz
Cost time: 2.269 sec
---------------start predicting input file: 003.nii.gz - 2/2 ----------------
Predicted lesion volume : 0.428 ml
Segmentation was saved to the file: 003.nii.gz
Cost time: 2.335 sec
-------------------------Completed--------------------------------------------------
Predictions infor is saved to predictions_info.csv
- Load the Docker image:
docker load -i viola_v2.tar.gz
- Run the inference with the following command:
docker run --name viola -v D:\data\CT\test\input\:/input -v D:\data\CT\test\predict\:/predict viola_v2:latest
├── /home/yourname/Desktop/input
├── 144.nii.gz
├── 145.nii.gz
├── 146.nii.gz
├── /home/yourname/Desktop/predict
├── 144.nii.gz
├── 145.nii.gz
├── 146.nii.gz
├── predictions_info.csv
Please consider citing our work if you find the code helps you
@inproceedings{liu2023ICH,
title={Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT},
author={Qinghui Liu and Bradley J MacIntosh and Till Schellhorn and Karoline Skogen and KyrreEeg Emblem and Atle Bjørnerud},
booktitle={Proceedings of ISBI 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
year={2023}
}