neuroslice 0.0.3

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 create a cuboid is available. The package supports three slice orientations (coronal, sagittal, and axial). 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 --direction 2 --mode cuboid --verbose

Arguments:

Mandatory:

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

Optional:

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

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", 1, 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_array, mask2cuboid_nifti

# From array
cuboid_mask = mask2cuboid_array(mask)

# From NIfTI file
mask2cuboid_nifti("mask.nii.gz", "cuboid_mask.nii.gz")

Combine multiple masks:

from neuroslice import unite_masks_array, unite_masks_nifti

# Combine arrays
combined = unite_masks_array(mask1, mask2, mask3, method="union")

# Combine NIfTI files
unite_masks_nifti(
    ["mask1.nii.gz", "mask2.nii.gz", "mask3.nii.gz"],
    "combined_mask.nii.gz",
    method="cuboid"
)

Advanced usage with direct predict function:

from neuroslice import predict
from ultralytics import YOLO
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, verbose=True)

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