https://github.com/FeliMe/brain_sas_baseline

Official implementation of the paper "Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI" accepted to the MICCAI 2021 BrainLes workshop
https://github.com/FeliMe/brain_sas_baseline

anomaly-detection anomaly-segmentation mri-brain

Last synced: 9 days ago
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Official implementation of the paper "Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI" accepted to the MICCAI 2021 BrainLes workshop

• Host: GitHub
• URL: https://github.com/FeliMe/brain_sas_baseline
• Owner: FeliMe
• License: mit
• Created: 2021-04-15T06:59:59.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-13T07:27:51.000Z (about 3 years ago)
• Last Synced: 2024-07-31T20:43:13.032Z (3 months ago)
• Topics: anomaly-detection, anomaly-segmentation, mri-brain
• Language: Python
• Homepage:
• Size: 36.1 KB
• Stars: 10
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• Unsupervised-Anomaly-Detection-in-Brain-MRI - [Code

README

        
# Semi-Supervised Anomaly Segmentation Baseline for Brain MRI

This repository contains the code for ["Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI"](https://arxiv.org/abs/2109.06023), accepted to the MICCAI 2021 BrainLes Workshop.

## Set-up

Clone the git project:

```

$ git clone https://github.com/FeliMe/brain_sas_baseline.git

```

Create a virtual environment and install the requirements:

```

$ conda create -f environment.yml

```

Activate the newly created environment:

```

$ conda activate anomaly_detection

```

## Download ROBEX and SRI ATLAS

Download and install ROBEX from https://www.nitrc.org/projects/robex

Download the SRI ATLAS from https://www.nitrc.org/projects/sri24/ and place it into DATAROOT/BrainAtlases/

## Download and pre-process Datasets

### BraTS

```

$ python download_data.py --dataset BraTS

$ python download_data.py --dataset BraTS --register

```

### MSLUB

```

$ python download_data.py --dataset MSLUB

$ python download_data.py --dataset MSLUB --skull_strip

$ python download_data.py --dataset MSLUB --register

```

### WMH

```

$ python download_data.py --dataset WMH

$ python download_data.py --dataset WMH --skull_strip

$ python download_data.py --dataset WMH --register

```

### MSSEG2015

```

$ python download_data.py --dataset MSSEG2015

$ python download_data.py --dataset MSSEG2015 --register

```

## Run the experiments (Here Experiment 1 from the paper on BraTS)

```

$ python baseline.py --test_ds BraTS --img_size 128 --slices_lower_upper 15 125

```