seqseg 0.1.6

A deep learning-based medical image blood vessel tracking and segmentation tool.

SeqSeg: Automatic Tracking and Segmentation of Blood Vessels in CT and MR Images

See paper here for detailed explanations and citation.

Below is an example showing the algorithm tracking and segmenting an abdominal aorta in 3D MR image scan:

Tutorial

Here is a tutorial on how to run the code, including installation instructions, downloading model weights, and running the segmentation pipeline on a medical image.

How it works

SeqSeg is a method for automatic tracking and segmentation of blood vessels in medical images. The algorithm uses a neural network to segment the vasculature locally and uses a tracking algorithm to take steps along the direction of the vessel and down bifurcation detected.

Here is the workflow of the algorithm:

where the neural network was trained on local subvolume patches of the image:

Set Up

Simply install SeqSeg using pip:

pip install seqseg

Check to see if the installation was successful by running:

seqseg --help

SeqSeg relies on nnU-Net for segmentation of the local medical image volumes. You will need model weights to run the algorithm - either use pretrained weights (available) or train a model yourself. After training a nnU-Net model, the weights will be saved in a nnUNet_results folder.

Main package dependencies:

Basic:

• Python 3.11

Machine Learning (Note: must be installed according to nnU-Net instructions):

• nnU-Net, nnunetv2=2.5.1
• Pytorch, torch=2.3.1

Image and Data Processing:

• SITK, simpleitk=2.2.1
• VTK, vtk=9.1.0
• PyYaml, pyyaml=6.0.1
• Matplotlib (optional)
• Pyyaml (optional)

and if using VMTK (not required):

• VMTK

Example setup using conda:

conda create -n seqseg python=3.11
conda activate seqseg
pip install seqseg

Example setup using pip (first create a virtual environment, see here):

python3 -m venv seqseg
source seqseg/bin/activate
pip install seqseg

Note: The code is tested with Python 3.11 and nnU-Net 2.5.1. If you are using a different version, please check the compatibility of the packages.

Running

See here for tutorial on how to run the code.

Set up data directory

Create a directory structure for your data as follows:

1. Images: Directory containing the medical images to be segmented. Image extension can be .nii.gz, .mha, .nrrd, or any of these.
2. Seeds: A seeds.json file containing the seed points for initialization.
3. Centerlines (optional): Directory containing centerline files if available.
4. Truths (optional): Directory containing ground truth segmentations if available. seqseg/tutorial/data/ ├── images ├── seeds.json ├── centerlines (if applicable)

SeqSeg requires a seed point for initialization. This can be given by either:

• seeds.json file: located in data directory (see sample under data)
• centerline: if centerlines are given, we initialize using the first points of the centerline
• cardiac mesh: then the aortic valve must be labeled as Region 8 and LV 7

Activate environment (eg. conda)

conda activate seqseg

or if using virtual environment:

source seqseg/bin/activate

Run

seqseg \
    -data_dir seqseg/tutorial/data/ \
    -nnunet_results_path nnUNet_results/ \
    -test_name 3d_fullres \
    -train_dataset Dataset005_SEQAORTANDFEMOMR \
    -fold 0 \
    -img_ext .mha \
    -config_name aorta_tutorial \
    -max_n_steps 5 \
    -max_n_branches 2 \
    -max_n_steps_per_branch 50 \
    -outdir output/ \
    -unit cm \
    -scale 1 \
    -start 0 \
    -stop -1

Note on units: typically the images used for training and tesing have the same units (e.g. mm or cm). If the units are different, you can set the scale argument to convert between the two. Here are the two examples where the units are different:

• If the nnUNet model was trained on mm and the testing data is in cm, then set scale=10.
• If the nnUNet model was trained on cm and the testing data is in mm, then set scale=0.1.

Details

seqseg: Main script to run.

Arguments:

-data_dir: This argument specifies the name of the folder containing the testing data (and test.json if applicable).

-test_name: This argument specifies the name of the nnUNet test to use. The default value is '3d_fullres'. Other possible values could be '2d', etc.

-train_dataset: This argument specifies the name of the dataset used to train the nnUNet model. For example, 'Dataset010_SEQCOROASOCACT'.

-'config_name': This argument specifies the name of the config file to use. The default value is 'global.yml'.

-fold: This argument specifies which fold to use for the nnUNet model. The default value is 'all'.

-img_ext: This argument specifies the image extension. For example, '.nii.gz'.

-outdir: This argument specifies the output directory where the results will be saved.

-scale: This argument specifies whether to scale image data. This is needed if the units for the nnUNet model and testing data are different. Example: if the nnUNet model was trained on mm and the testing data is in cm, then set scale=10. The default value is 1.

-start: This argument specifies where to start in the list of testing samples. The default value is 0.

-stop: This argument specifies where to stop in the list of testing samples. The default value is -1, which means to process all samples until the end of the list.

-max_n_steps: This argument specifies the maximum number of steps to run the algorithm. The default value is 1000.

-unit: This argument specifies the unit of the image data. The default value is 'cm'.

Config file

config/xx.yml: File contains config parameters, default is set but can be changed depending on task

We recommend duplicating the file and changing the name to avoid overwriting the default values. If so, the config file must be passed as an argument when running the script: config_name

Citation

When using SeqSeg, please cite the following paper:

@Article{SveinssonCepero2024,
author={Sveinsson Cepero, Numi
and Shadden, Shawn C.},
title={SeqSeg: Learning Local Segments for Automatic Vascular Model Construction},
journal={Annals of Biomedical Engineering},
year={2024},
month={Sep},
day={18},
issn={1573-9686},
doi={10.1007/s10439-024-03611-z},
url={https://doi.org/10.1007/s10439-024-03611-z},
}