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Hypothalamus pipeline

Hypothalamic subfields segmentation pipeline

Input

  • T1w image, a T2w image, or both images. Note: Input images to the tool need to be Bias-Field corrected.

Requirements

  • Same as FastSurfer.
  • If the T1w and T2w images are available and not co-registered, FreeSurfer should be sourced to run the registration code, and the mri_coreg and mri_vol2vol binaries should also be available.

Model weights

Note: These weights (version 1.1) are retrained compared to paper (version 1.0) for better rotation generalization, performance is equivalent.

Pipeline Steps

  1. Registration (optional, only required for multi-modal input)
  2. Hypothalamus Segmentation

Running the tool

  • The HypVINN output can be obtained by running the default run_fastsurfer.sh script (for more information see FastSurfer documentation).
  • HypVINN can also be run independently by running HypVINN/run_prediction.py, however we recommend running the whole FastSurfer pipeline as it includes all the required pre-processing steps.
  • HypVINN has the following arguments:

Input and output arguments

  • --sid <name> : Subject ID, the subject data upon which to operate
  • --sd <name> : Directory in which evaluation results should be written.
  • --t1 </dir/T1**.nii.gz> : T1 image path
  • --t2 </dir/T2**.nii.gz> : T2 image path
  • --seg_log : Path to file in which run logs will be saved. If not set logs will be stored in /sd/sid/scripts/hypvinn_seg.log

Image processing options

  • --reg_mode : Ignored, if no T2 image is passed. Specifies the registration method used to register T1 and T2 images. Options are 'coreg' (default) for mri_coreg, 'robust' for mri_robust_register, and 'none' to skip registration (this requires T1 and T2 are externally co-registered).
  • --qc_snap: Activate the creation of QC snapshots of the predicted HypVINN segmentation in /sd/sid/qc_snapshots. The created QC snapshots are created to simplify the visual quality control process.

FastSurfer Technical parameters (see FastSurfer documentation)

  • --device
  • --viewgg_device
  • --threads
  • --batch_size
  • --async_io
  • --allow_root

Checkpoint to load

  • --ckpt_cor </dir/to/coronal/ckpt> : Coronal checkpoint to load, default = $FASTSURFER_ROOT/checkpoints/HypVINN_axial_v1.1.0.pkl
  • --ckpt_ax </dir/to/axial/ckpt> : Axial checkpoint to load, default = $FASTSURFER_ROOT/checkpoints/HypVINN_coronal_v1.1.0.pkl
  • --ckpt_sag </dir/to/sagittal/ckpt> : Sagittal checkpoint to load, default = $FASTSURFER_ROOT/checkpoints/HypVINN_sagittal_v1.1.0.pkl

CFG-file with default options for network

  • --cfg_cor </dir/to/coronal/cfg> : Coronal config file to load, default = $FASTSURFER_ROOT/HypVINN/config/HypVINN_coronal_v1.1.0.yaml
  • --cfg_ax </dir/to/axial/cfg> : Axial config file to load, default = $FASTSURFER_ROOT/HypVINN/config/HypVINN_axial_v1.1.0.yaml
  • --cfg_sag </dir/to/sagittal/cfg> : Sagittal config file to load, default = $FASTSURFER_ROOT/HypVINN/config/HypVINN_sagittal_v1.1.0.yaml

Usage

  • The Hypothalamus pipeline can be run by using a T1 a T2 or both images.
  • Is recommended that all input images are bias field corrected and when passing both T1 and T2 they need to be co-registered.
  • The Hypvinn pipeline can do the registration by itself (step 1). This step can be skipped if images are already registered externally.
  • Bias field-corrected images are generated by default by the FastSurfer pipeline; therefore, we recommend running the entire FastSurfer pipeline. If you already have a subject's FastSurfer output without Hypvinn output, check example 5.

  1. Run HypVINN pipeline

    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                 --t1 /data/test_subject_t1_bias_field_corrected.nii.gz \
                                 --t2 /data/test_subject_t2_bias_field_corrected.nii.gz \
                                 --reg_mode coreg \
                                 --batch_size 6

  2. Run HypVINN pipeline only using a t1

    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                 --t1 /data/test_subject_t1_bias_field_corrected.nii.gz \
                                 --batch_size 6

  3. Run HypVINN pipeline without the registration step

    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                 --t1 /data/test_subject_t1_bias_field_corrected.nii.gz \
                                 --t2 /data/test_subject_t2_bias_field_corrected_and_coregistered_to_t1.nii.gz \
                                 --reg_mode none \
                                 --batch_size 6

  4. Run HypVINN pipeline with creation of qc snapshots

    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                 --t1 /data/test_subject_t1_bias_field_corrected.nii.gz \
                                 --t2 /data/test_subject_t2_bias_field_corrected.nii.gz \
                                 --reg_mode coreg \
                                 --batch_size 6 --qc_snap

  5. Run HypVINN pipeline from an existing FastSurfer subject output -- recommended when FastSurfer output is there without the Hypothalamus module

    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                 --t1 /output/test_subject/mri/orig_nu.mgz \
                                 --seg_log /output/test_subject/scripts/deep-seg.log \
                                 --batch_size 6 --qc_snap

Output

#Output Scheme 
|-- output_dir
    |--sid                                 
        |-- mri : MRI outputs
            |--hypothalamus.HypVINN.nii.gz(Hypothalamus Segmentation)
            |-- hypothalamus_mask.HypVINN.nii.gz (Hypothalamus Segmentation Mask)
            |-- transforms
                |-- t2tot1.lta (FreeSurfer registration file, only available if registration is performed)
        |-- qc_snapshots : QC outputs (optional)
            |-- hypothalamus.HypVINN_qc_screenshoot.png (Coronal quality control image)
        |-- stats : Statistics outputs                                                 
            |-- hypothalamus.HypVINN.stats (Segmentation stats)

Developer

Santiago Estrada : santiago.estrada@dzne.de

Citation

If you use the HypVINN module please cite

Santiago Estrada, David Kügler, Emad Bahrami, Peng Xu, Dilshad Mousa, Monique M.B. Breteler, N. Ahmad Aziz, Martin Reuter; 
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI. 
Imaging Neuroscience 2023; 1 1–32. doi: https://doi.org/10.1162/imag_a_00034