simcortexpp 0.1.3

SimCortexPP (SCPP) is a modular pipeline for cortical surface reconstruction (preproc, seg, initsurf, deform)

SimCortexPP (SCPP)

SimCortexPP (SCPP) is a modular pipeline for cortical surface reconstruction in MNI space. It provides four stages:

1. Preprocessing (FreeSurfer to MNI152)
   Export key FreeSurfer volumes/surfaces, register them to MNI152, and write outputs in a BIDS-derivatives-style layout.

2. Segmentation (3D U-Net, MNI space)
   Train and apply a 3D U-Net to predict a 9-class segmentation in MNI152 space, with inference and evaluation utilities.

3. Initial Surfaces (InitSurf)
   Generate initial White Matter and Pial surfaces from segmentation predictions (plus ribbon SDF/probability outputs).

4. Deformation (Deform)
   Deform the initial surfaces toward MNI-aligned FreeSurfer surfaces using geometric losses and optional collision metrics, and write deformed surfaces as BIDS-derivatives.

This README focuses on how to run the pipeline correctly (inputs, outputs, folder/file naming, and commands).

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Table of Contents

• Installation
• Configuration
• Data and Folder Conventions
• Split File Format
• Stage 1 - Preprocessing (FreeSurfer to MNI152)
• Stage 2 - Segmentation (3D U-Net, MNI space)
• Stage 3 - Initial Surfaces (InitSurf)
• Stage 4 - Deformation (Deform)

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Installation

From the repository root:

pip install -e .
scpp --help
scpp seg --help
scpp initsurf --help
scpp deform --help

Recommended environment

• Python 3.10+
• PyTorch + MONAI
• nibabel, numpy, pandas, openpyxl
• trimesh, scipy, tqdm
• External tools for Stage 1: NiftyReg (reg_aladin, reg_resample)
• Optional (Deform metrics):
  • python-fcl for collision metrics
  • pymeshlab for SIF (self-intersection fraction) in Deform evaluation

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Configuration

All stages use Hydra YAML configs shipped with the package (see src/simcortexpp/configs/<stage>/*.yaml).

You have two ways to configure a run:

1. Edit the stage YAML (recommended for longer runs / stable experiments), then run commands with no extra arguments, e.g.:

   • scpp deform eval

2. Use Hydra overrides on the CLI (recommended for quick tests), e.g.:

   • scpp deform eval dataset.split_name=test outputs.out_dir=/tmp/deform_eval

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Data and Folder Conventions

You will typically work with two roots:

1. Code repository (this repository)
   Contains code, configs, and scripts (no data).

2. Dataset root (BIDS + derivatives)
   Each dataset has its own root directory. Recommended structure:

datasets/<dataset-name>/
  bids/                 # raw BIDS dataset
  derivatives/          # processed outputs (BIDS derivatives)
    freesurfer-7.4.1/
    scpp-preproc-0.1/
    scpp-seg-0.1/
    scpp-initsurf-0.1/
    scpp-deform-0.1/
  splits/
    <dataset>_split.csv

SCPP reads inputs from derivatives/ and writes outputs back to derivatives/ using BIDS-derivatives-style naming.

Important: keep the dataset root naming consistent (e.g., use datasets/... everywhere).

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Split File Format

A split CSV is required for Segmentation, InitSurf, and Deform.

Single-dataset split

Minimal columns:

• subject (e.g., sub-0001)
• split in {train, val, test}

Multi-dataset split

Include an additional column:

• dataset (string key that matches config keys, e.g., HCP_YA, OASIS1)

Example:

subject,split,dataset
sub-100307,test,HCP_YA
sub-101915,test,HCP_YA
sub-0001,test,OASIS1

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Stage 1 - Preprocessing (FreeSurfer to MNI152)

This stage exports key FreeSurfer outputs (volumes + surfaces), registers them to MNI152, and writes results to a BIDS-derivatives-style folder.

Inputs

• FreeSurfer derivatives root (contains sub-* folders)
• MNI template (e.g., src/MNI152_T1_1mm.nii.gz)

Dependencies (system tools)

• NiftyReg: reg_aladin, reg_resample must be in PATH
• FreeSurfer tools are recommended (e.g., mri_convert, mris_convert) for consistent conversions

Run (all subjects discovered automatically)

scpp fs-to-mni   --freesurfer-root /path/to/datasets/<dataset>/derivatives/freesurfer-7.4.1   --out-deriv-root  /path/to/datasets/<dataset>/derivatives/scpp-preproc-0.1   --mni-template    /path/to/SimCortexPP/src/MNI152_T1_1mm.nii.gz   --decimate 0.3   -v

Run (selected subjects)

scpp fs-to-mni   --freesurfer-root /path/to/datasets/<dataset>/derivatives/freesurfer-7.4.1   --out-deriv-root  /path/to/datasets/<dataset>/derivatives/scpp-preproc-0.1   --mni-template    /path/to/SimCortexPP/src/MNI152_T1_1mm.nii.gz   -p sub-0001 -p sub-0019   -v

Output layout (example)

scpp-preproc-0.1/
  dataset_description.json
  sub-XXXX/
    ses-01/
      anat/
        sub-XXXX_ses-01_space-MNI152_desc-preproc_T1w.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-aparc+aseg_dseg.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-filled_T1w.nii.gz
        sub-XXXX_ses-01_from-T1w_to-MNI152_mode-image_xfm.txt
      surfaces/
        sub-XXXX_ses-01_space-MNI152_hemi-L_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-L_pial.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-R_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-R_pial.surf.ply

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Stage 2 - Segmentation (3D U-Net, MNI space)

This stage trains and applies a 3D U-Net to predict a 9-class segmentation in MNI152 space using Stage 1 outputs.

Expected inputs (from Stage 1)

Under scpp-preproc-*, for each subject:

• ..._desc-preproc_T1w.nii.gz
• ..._desc-aparc+aseg_dseg.nii.gz
• ..._desc-filled_T1w.nii.gz

Output naming (predictions)

Predictions are written under scpp-seg-*:

• sub-XXXX/ses-01/anat/sub-XXXX_ses-01_space-MNI152_desc-seg9_dseg.nii.gz

Train (single GPU)

scpp seg train   dataset.path=/path/to/datasets/<dataset>/derivatives/scpp-preproc-0.1   dataset.split_file=/path/to/datasets/<dataset>/splits/<dataset>_split.csv   outputs.root=/path/to/scpp-runs/seg/exp01   trainer.use_ddp=false

Train (multi-GPU, torchrun)

scpp seg train --torchrun --nproc-per-node 2   dataset.path=/path/to/datasets/<dataset>/derivatives/scpp-preproc-0.1   dataset.split_file=/path/to/datasets/<dataset>/splits/<dataset>_split.csv   outputs.root=/path/to/scpp-runs/seg/exp01   trainer.use_ddp=true

Inference

scpp seg infer   dataset.path=/path/to/datasets/<dataset>/derivatives/scpp-preproc-0.1   dataset.split_file=/path/to/datasets/<dataset>/splits/<dataset>_split.csv   dataset.split_name=test   model.ckpt_path=/path/to/seg_best_dice.pt   outputs.out_root=/path/to/datasets/<dataset>/derivatives/scpp-seg-0.1

Evaluation (multi-dataset example)

scpp seg eval   dataset.split_file=/path/to/datasets/splits/dataset_split.csv   dataset.split_name=test   dataset.roots.HCP_YA=/path/to/datasets/hcpya-u100/derivatives/scpp-preproc-0.1   dataset.roots.OASIS1=/path/to/datasets/oasis-1/derivatives/scpp-preproc-0.1   outputs.pred_roots.HCP_YA=/path/to/datasets/hcpya-u100/derivatives/scpp-seg-0.1   outputs.pred_roots.OASIS1=/path/to/datasets/oasis-1/derivatives/scpp-seg-0.1   outputs.eval_csv=/path/to/scpp-runs/seg/exp01/evals/seg_eval_test.csv   outputs.eval_xlsx=/path/to/scpp-runs/seg/exp01/evals/seg_eval_test.xlsx

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Stage 3 — Initial Surfaces (InitSurf)

This stage generates initial cortical surfaces from saved segmentation predictions (not end-to-end).

Inputs

• Preproc roots (scpp-preproc-*) for MNI T1
• Seg roots (scpp-seg-*) for ..._desc-seg9_dseg.nii.gz
• Split CSV (same format as Stage 2)

Outputs

BIDS-derivatives-style outputs under scpp-initsurf-* (meshes + SDF volumes + ribbon prob):

scpp-initsurf-0.1/
  dataset_description.json
  sub-XXXX/
    ses-01/
      anat/
        sub-XXXX_ses-01_space-MNI152_desc-lh_white_sdf.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-rh_white_sdf.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-lh_pial_sdf.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-rh_pial_sdf.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-ribbon_sdf.nii.gz
        sub-XXXX_ses-01_space-MNI152_desc-ribbon_prob.nii.gz
      surfaces/
        sub-XXXX_ses-01_space-MNI152_hemi-L_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-L_pial.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-R_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_hemi-R_pial.surf.ply

Run (multi-dataset example)

scpp initsurf generate   dataset.split_file=/path/to/datasets/splits/dataset_split.csv   dataset.split_name=all   dataset.roots.HCP_YA=/path/to/datasets/hcpya-u100/derivatives/scpp-preproc-0.1   dataset.roots.OASIS1=/path/to/datasets/oasis-1/derivatives/scpp-preproc-0.1   dataset.seg_roots.HCP_YA=/path/to/datasets/hcpya-u100/derivatives/scpp-seg-0.1   dataset.seg_roots.OASIS1=/path/to/datasets/oasis-1/derivatives/scpp-seg-0.1   outputs.out_roots.HCP_YA=/path/to/datasets/hcpya-u100/derivatives/scpp-initsurf-0.1   outputs.out_roots.OASIS1=/path/to/datasets/oasis-1/derivatives/scpp-initsurf-0.1   outputs.log_dir=/path/to/scpp-runs/initsurf/exp01/logs_generate

Typical runtime: ~31 s/subject (hardware-dependent).

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Stage 4 - Deformation (Deform)

This stage deforms InitSurf meshes using input volumes and geometric losses, and writes deformed surfaces to a BIDS-derivatives folder.

Inputs

• Preproc root (scpp-preproc-*): MNI T1 + GT FreeSurfer surfaces in MNI space
• InitSurf root (scpp-initsurf-*): ribbon probability + initial surfaces
• Split CSV (same format as Stage 2)

Outputs

Deformed surfaces under scpp-deform-*:

scpp-deform-0.1/
  dataset_description.json
  sub-XXXX/
    ses-01/
      surfaces/
        sub-XXXX_ses-01_space-MNI152_desc-deform_hemi-L_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_desc-deform_hemi-L_pial.surf.ply
        sub-XXXX_ses-01_space-MNI152_desc-deform_hemi-R_white.surf.ply
        sub-XXXX_ses-01_space-MNI152_desc-deform_hemi-R_pial.surf.ply

Train (multi-GPU example)

scpp deform train --torchrun --nproc-per-node 2   outputs.root=/path/to/scpp-runs/deform/exp01

Inference

scpp deform infer

Evaluation

scpp deform eval

Evaluation writes four Excel files:

• surface_metrics.xlsx
• collision_metrics.xlsx
• collision_metrics_enhanced.xlsx
• collision_summary.xlsx

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Outputs Summary

For each dataset root:

• derivatives/scpp-preproc-0.1/
  MNI T1, aparc+aseg, filled, transforms, and MNI-aligned FreeSurfer surfaces.

• derivatives/scpp-seg-0.1/
  9-class segmentation predictions (*_desc-seg9_dseg.nii.gz).

• derivatives/scpp-initsurf-0.1/
  Initial surfaces (*.surf.ply) + SDF volumes + ribbon SDF/probability.

• derivatives/scpp-deform-0.1/
  Deformed surfaces (*_desc-deform_*.surf.ply).

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