Brain Hippocampus Segmentation
This program segments the Hippocampus of raw brain T1 images in a few seconds.
It relies on a Convolutional Neural Network pre-trained on thousands of images from multiple large cohorts, and is therefore quite robust to subject- and MR-contrast variation. For more details on its creation, refer the corresponding manuscript at http://dx.doi.org/10.1016/j.media.2017.11.004
This official hippodeep version is a modern PyTorch port of the original Theano version that is technically obsolete. While the hippocampal segmentation model is exactly the same as described in the paper, the pre- and post-processing steps had been improved, and thus, results may differ sligthly. The deprecated theano repo is still available at https://github.com/bthyreau/hippodeep
This program requires Python 3 with the PyTorch library.
No GPU is required.
It should work on most distro and platform that supports pytorch, though mainly tested on Linux CentOS 6.x ~ 8.x, Ubuntu 18.04 ~ 22.04 and MacOS 10.13 ~ 12, using PyTorch versions from 1.0.0 to 2.6.0.
Just clone or download this repository.
If you have the uv packaging tool ( https://docs.astral.sh/uv/ ), you can do
uv run hippodeep.py example_brain_t1.nii.gz
which should take care of downloading the dependencies in the first run.
Otherwise, you need to configure a python 3 environment on your machine: In addition to PyTorch, the code requires scipy and nibabel. A possible way to install python from scratch is to use Anaconda (anaconda.com) to create an environment, then
- install scipy (
conda install scipyorpip install scipy) and nibabel (pip install nibabel) - get pytorch for python from
https://pytorch.org/get-started/locally/. CUDA is not necessary. - Then, to use the program, call it with
python hippodeep.py example_brain_t1.nii.gz, possibly changing 'python' to 'python3' depending on your exact setup.
To process multiple subjects, pass them as multiple arguments. e.g:
python hippodeep.py subject_*.nii.gz. (or equivalent with 'uv run')
The resulting segmentations should be stored as example_brain_t1_mask_L.nii.gz (or R for right) and example_brain_t1_brain_mask.nii.gz. The mask volumes (in mm^3) are stored in a csv file named example_brain_t1_hippoLR_volumes.csv. If more than one input was specified, a summary table named all_subjects_hippo_report.csv is created.
MIT License