neuroslice 1.0.1

Slice-wise brain tumor detection and 3D mask reconstruction from MRI

Neuroslice

A Python package for brain tumor segmentation using YOLO models on MRI FLAIR data.

Table of Contents

• Description
• Installation
• Usage
  • Command Line Interface
  • Python API
• Contributing
• Citation

Description

Neuroslice provides automated brain tumor bounding box detection using pre-trained YOLO models. It uses FLAIR images to segment slice wise the image, though an option to afterwards create a cuboid is available. The package supports three slice orientations (coronal, sagittal, and axial) as well as combinations of them. Models are automatically downloaded from Hugging Face when first used.

Installation

You can install Neuroslice using pip:

pip install neuroslice

For development installation:

git clone https://github.com/anamatoso/neuroslice.git
cd neuroslice
pip install -e .

Usage

Command Line Interface

Basic usage with default settings (coronal direction, union mode):

neuroslice input.nii.gz output_mask.nii.gz

Specify slice direction and processing mode:

neuroslice input.nii.gz output_mask.nii.gz --axis 2 --mode cuboid --verbose

Use the combination of diferent orientations:

neuroslice input.nii.gz output_mask.nii.gz --axis 0,1

Arguments:

Mandatory:

• input: Path to input NIfTI file (.nii or .nii.gz)
• output: Path to output mask NIfTI file

Optional:

• --direction: Slice axis (RAS) - 0 (sagittal), 1 (coronal, default), 2 (axial)
• --mode: Processing mode - union (default) or cuboid (bounding box)
• --verbose: Print detailed statistics

Python API

Generate a tumor mask:

from neuroslice import predict_mask
import nibabel as nib

# Generate mask from NIfTI file
mask = predict_mask("input.nii.gz", axis=2, verbose=True)

# Save the mask
nifti = nib.load("input.nii.gz")
output = nib.Nifti1Image(mask.astype("uint8"), nifti.affine, nifti.header)
nib.save(output, "output_mask.nii.gz")

Convert mask to bounding cuboid:

from neuroslice import mask2cuboid

cuboid_mask = mask2cuboid(mask)

Combine multiple masks:

from neuroslice import unite_masks

combined = unite_masks(mask1, mask2, mask3)

Advanced usage with direct predict function:

from neuroslice import predict, predict_multi_axis
import nibabel as nib

# Load your data
nifti = nib.load("input.nii.gz")
data = nifti.get_fdata()

# Generate mask with custom axis 
mask = predict(data, axis=0, mode="union", verbose=True)

mask_cuboid = predict_multi_axis(data, axis=[0,1], mode="cuboid"):

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your branch (git checkout -b feature/AmazingFeature)
3. Add your changes to the branch
4. Commit your changes (git commit -m 'Add some AmazingFeature')
5. Push to the branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

Citation

If you use Neuroslice in your research, please cite:

TBD