pardesline

pardesline: 3D computer vision and medical imaging datasets, algorithms and models for Python

Load 43 public 3D and medical datasets in one line, run 73 algorithms on point clouds, meshes and CT/MRI volumes with defaults that hold on real scans, hand the result to Open3D, PyVista or SimpleITK, or to an AI agent through MCP.

Ten pipelines, one package: the showcase has the snippet, the expert skill and the MCP tool for each.

30-second demo: dataset in, registration out

pip install "pardesline[all]"

import pardesline as pl

import pyvista as pv

s = pl.load("rire_brain")[0] # CT + MR T1 of one head (19 MB); the licence line is printed, never guessed moved = s.ct.align(s.mr_t1) # rigid registration: SimpleITK, Mattes mutual information, catalog defaults print(moved.transform"sub_transforms"["rotation_deg"]) # [0.34, -0.35, -4.38] degrees

ct, mr = s.ct.to_pyvista(), moved.to_pyvista() # the libraries you already use, one call away p = pv.Plotter(shape=(1, 2)) p.subplot(0, 0) p.add_mesh(ct.slice("z"), cmap="gray") p.add_text("CT (fixed)") p.subplot(0, 1) p.add_mesh(ct.slice("z"), cmap="gray") p.add_mesh(mr.slice("z"), cmap="magma", opacity=0.55) p.add_text("MR T1 registered onto the CT") p.show()

Measured cold on a laptop: 7.6 s download, 8.8 s registration, 21.5 s total. The script is examples/register_ct_mr_rire/run.py.

Why this exists

• Datasets are scattered and incompatible. The Decathlon is a tar on S3, TCIA is a REST API that returns zips of DICOM, KiTS is on Hugging Face, KITTI is a zip of headerless binaries. pardesline describes each one in a manifest (files, roles, labels, frame, units, licence, citation) and fetches lazily, by HTTP Range inside archives when it can, so one sample costs one sample.
• Nothing carries its meaning. A .bin is numbers until someone says (N, 4) float32, x y z reflectance, metres. A NIfTI label map is integers until someone says 1 = anterior. Manifests carry that; your own folders get it through pl.scan, which asks what it cannot know instead of guessing.
• Open3D, PyVista and SimpleITK do not talk to each other. One typed object (PointCloud, Mesh, Volume, LabelMap) converts between them, keeps frame, units, labels, licence and provenance, and exposes to_open3d(), to_pyvista(), to_sitk() when you want the library itself.
• Textbook defaults break on real scans. The ICP that converges on the tutorial fragments ends 32 degrees off on a noisy partial scan while reporting a 0.98 fitness. Every algorithm here runs behind one call with defaults tied to the data (voxel size from the bounding box, correspondence distances as multiples of it) and the sidecar records what was used. See Defaults that work on real data.
• Models do not plug into datasets. The expert skills and reference scripts (PointNet, DINOv3 features, JEPA, VAE, nnU-Net preparation) read any pardesline dataset. One-line access to pretrained models through the same manifest pattern is the next step on the roadmap.

Install

pip install pardesline                 # core: catalog, object model, datasets, CLI (no native libraries)

pip install "pardesline[local]" # + Open3D, PyVista, SciPy, scikit-image: point clouds, meshes, LiDAR, SDF pip install "pardesline[medical]" # + SimpleITK: volumes, DICOM series, registration, segmentation pip install "pardesline[hf]" # + huggingface_hub: datasets on the Hub pip install "pardesline[mcp]" # + mcp: the local server for AI agents pip install "pardesline[all]" # everything above

[local] needs Open3D wheels: 0.20 covers Python 3.10 – 3.13, 0.19 covers 3.10 – 3.12. pardesline 0.2.0 on PyPI pins Open3D below 0.20, so on Python 3.13 use 3.12 or install from main until 0.3.0 ships. Conda users: conda create -n pl python=3.12 && pip install "pardesline[all]" (Open3D and SimpleITK come from PyPI). Troubleshooting: docs/running-modes.md.

📦 Datasets

One loader for every source. Files are fetched on access and verified; the licence and the citation travel with the data; ds.describe() says what each role is, in which frame and units, and what the labels mean.

ds = pl.load("totalsegmentator_ct")  # 1 228 CTs, 117 structures, 23 GB zip on Zenodo: indexed with 14 MB

ct = ds.take(1)[0].ct # one 50 MB volume fetched by HTTP Range, nothing else ds = pl.load( "hf://neheller/KiTS-Challenge-Imaging" ) # any Hugging Face repo: a manifest is drafted and confirmed ds = pl.load("tcia://Pancreas-CT") # any public TCIA collection: DICOM series as folders ds = pl.scan("/data/my_study") # your folder: drafts a manifest, asks for units / labels / licence

Medical (35)

| [Medical Segmentation Decathlon, Task02 Heart: left-atrium segmentation on 30 cardi