The Torso Processing ToolBox (TPTBox) is a multi-functional package to handle any sort of bids-conform dataset (CT, MRI, ...) It can find, filter, search any BIDS_Family and subjects, and has many functionalities, among them:

• Easily loop over datasets, and the required files
• Read, Write Niftys, centroid jsons, ...
• Reorient, Resample, Shift Niftys, Centroids, labels
• Modular 2D snapshot generation (different views, MIPs, ...)
• 3D Mesh generation from segmentation and snapshots from them
• Registration
• Logging everything consistently
• ...

Install the package

conda create -n 3.10 python=3.10
conda activate 3.10
pip install TPTBox
# Optional dependency Registration
pip install hf-deepali

Install via github:

(you should be in the project folder)

pip install poetry
poetry install

or: Develop mode is really, really nice:

pip install poetry
poetry install --with dev

Functionalities

Each folder in this package represents a different functionality.

The top-level-hierarchy incorporates the most important files, the BIDS_files.

BIDS_Files

This file builds a data model out of the BIDS file names. It can load a dataset as a BIDS_Global_info file, from which search queries and loops over the dataset can be started. See tutorial_BIDS_files.ipynb for details.

bids_constants

Defines constants for the BIDS nomenclature (sequence-splitting keys, naming conventions...)

vert_constants

Contains definitions and sort order for our intern labels, for vertebrae, POI, ...

Rotation and Resampling

Example rotate and resample.

from TPTBox import NII

nii = NII.load("...path/xyz.nii.gz", seg=True)
# R right, L left
# S superior/up, I inferior/down
# A anterior/front, P posterior/back
img_rot = nii.reorient(axcodes_to=("P", "I", "R"))
img_scale = nii.rescale((1.5, 5, 1))  # in mm as currently rotated
# resample to an other image
img_resampled_to_other = nii.resample_from_to(img_scale)

nii.get_array()  # get numpy array
nii.affine  # Affine matrix
nii.header  # NIFTY header
nii.orientation  # Orientation in 3-Letters
nii.zoom # Scale of the three image axis
nii.shape #shape

Stitching

Python function and script for arbitrary image stitching. See Details

Spineps and Points of Interests (POI) integration

For our Spine segmentation pipline follow the installation of SPINEPS. Image Source: Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine;

SPINEPS will produce two mask: instance and semantic labels. With these we can compute our POIs. There are either center of mass points or surface points with bioloical meaning. See Validation of a Patient-Specific Musculoskeletal Model for Lumbar Load Estimation Generated by an Automated Pipeline From Whole Body CT

from TPTBox import NII, POI, Location, POI_Global, calc_poi_from_subreg_vert
from TPTBox.core.vert_constants import v_name2idx
from TPTBox.segmentation.spineps import run_spineps_single

# This requires that spineps is installed
output_paths = run_spineps_single(
    "file-path-of_T2w.nii.gz",
    model_semantic="t2w",
    ignore_compatibility_issues=True,
)
out_spine = output_paths["out_spine"]
out_vert = output_paths["out_vert"]
semantic_nii = NII.load(out_spine, seg=True)
instance_nii = NII.load(out_vert, seg=True)

poi = calc_poi_from_subreg_vert(
    instance_nii,
    semantic_nii,
    subreg_id=[
        Location.Vertebra_Full,
        Location.Arcus_Vertebrae,
        Location.Spinosus_Process,
        Location.Costal_Process_Left,
        Location.Costal_Process_Right,
        Location.Superior_Articular_Left,
        Location.Superior_Articular_Right,
        Location.Inferior_Articular_Left,
        Location.Inferior_Articular_Right,
        # Location.Vertebra_Corpus_border, CT only
        Location.Vertebra_Corpus,
        Location.Vertebra_Disc,
        Location.Muscle_Inserts_Spinosus_Process,
        Location.Muscle_Inserts_Transverse_Process_Left,
        Location.Muscle_Inserts_Transverse_Process_Right,
        Location.Muscle_Inserts_Vertebral_Body_Left,
        Location.Muscle_Inserts_Vertebral_Body_Right,
        Location.Muscle_Inserts_Articulate_Process_Inferior_Left,
        Location.Muscle_Inserts_Articulate_Process_Inferior_Right,
        Location.Muscle_Inserts_Articulate_Process_Superior_Left,
        Location.Muscle_Inserts_Articulate_Process_Superior_Right,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Superior_Median,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Superior_Median,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Inferior_Median,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Inferior_Median,
        Location.Additional_Vertebral_Body_Middle_Superior_Median,
        Location.Additional_Vertebral_Body_Posterior_Central_Median,
        Location.Additional_Vertebral_Body_Middle_Inferior_Median,
        Location.Additional_Vertebral_Body_Anterior_Central_Median,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Superior_Left,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Superior_Left,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Inferior_Left,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Inferior_Left,
        Location.Additional_Vertebral_Body_Middle_Superior_Left,
        Location.Additional_Vertebral_Body_Posterior_Central_Left,
        Location.Additional_Vertebral_Body_Middle_Inferior_Left,
        Location.Additional_Vertebral_Body_Anterior_Central_Left,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Superior_Right,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Superior_Right,
        Location.Ligament_Attachment_Point_Anterior_Longitudinal_Inferior_Right,
        Location.Ligament_Attachment_Point_Posterior_Longitudinal_Inferior_Right,
        Location.Additional_Vertebral_Body_Middle_Superior_Right,
        Location.Additional_Vertebral_Body_Posterior_Central_Right,
        Location.Additional_Vertebral_Body_Middle_Inferior_Right,
        Location.Additional_Vertebral_Body_Anterior_Central_Right,
        Location.Ligament_Attachment_Point_Flava_Superior_Median,
        Location.Ligament_Attachment_Point_Flava_Inferior_Median,
        Location.Vertebra_Direction_Posterior,
        Location.Vertebra_Direction_Inferior,
        Location.Vertebra_Direction_Right,
    ],
)
poi = poi.round(2)
print("Vertebra T4 Vertebra Corpus Center of mass:", poi[v_name2idx["T4"], Location.Vertebra_Corpus])
print("The id number of T4 Vertebra_Corpus is ", v_name2idx["T4"], Location.Vertebra_Corpus.value)

# rescale/reorante local poi like nii
poi_new = poi.reorient(("P", "I", "R")).rescale((1, 1, 1))
# Local and global POIs can be rescaled to a target spacing with:
poi_new = poi.resample_from_to(other_nii_or_poi)

# local to global poi
global_poi = poi.to_global(itk_coords=True)
# You can save global pois in mrk.json format for import and editing in slicer.
global_poi.save_mrk("FILE.mrk.json", glyphScale=3.0)
# Import as a Markup in slicer; To make points editable you must click on the "lock" symbol under Markups - Control Points - Interaction

# Save in our format:
poi.save(poi_path)
# Loading local/global Poi
poi = POI.load(poi_path)
poi = POI_Global.load(poi_path)

Snapshot2D Spine

The snapshot function automatically generates sag, cor, axial cuts in the center of a segmentation.

from TPTBox.spine.snapshot2D import Snapshot_Frame, create_snapshot

ct = Path("Path to CT")
mri = Path("Path to MRI")
vert = Path("Path to Vertebra segmentation")
subreg = Path("Path to Vertebra subregions")
poi_ct = Path("Path to Vertebra poi")
poi_mr = Path("Path to Vertebra poi")

ct_frame = Snapshot_Frame(image=ct, segmentation=vert, centroids=poi_ct, mode="CT", coronal=True, axial=True)
mr_frame = Snapshot_Frame(image=mri, segmentation=vert, centroids=poi_mr, mode="MRI", coronal=True, axial=True)
create_snapshot(snp_path="snapshot.jpg", frames=[ct_frame, mr_frame])

Snapshot3D

Requires additonal python packages: vtk fury xvfbwrapper

from TPTBox.mesh3D.snapshot3D import make_snapshot3D, make_snapshot3D_parallel

# all segmentation; view give the rotation of an image
make_snapshot3D("sub-101000_msk.nii.gz", "snapshot3D.png", view=["A", "L", "P", "R"])
# Select witch segmentation per panel are chosen.
make_snapshot3D("sub-101000_msk.nii.gz", "snapshot3D_v2.png", view=["A"], ids_list=[[1, 2], [3]])
# we proviede a implementation to process multiple images at the same time.
make_snapshot3D_parallel(["a.nii.gz", "b.nii.gz"], ["snp_a.png", "snp_b.png"], view=["A"])