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AneuG-Flow

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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 839, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte

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AneuG-Flow Dataset

Dataset Description

AneuG-Flow is a comprehensive dataset for intracranial aneurysm hemodynamics, containing computational fluid dynamics (CFD) simulations of blood flow through synthetic aneurysm morphologies generated by a generative model AneuG [1].

Dataset Summary

This dataset provides:

  • 730 aneurysm cases with transient (time-varying) flow simulations
  • High-resolution 3D geometries (original and remeshed)
  • Blood flow data and wall shear stress
  • Flow split ratios and inlet boundary conditions
  • Pre-processed and assembled data for machine learning applications

Dataset Structure

Data Instances

The dataset is organized into three main directories:

1. steady_data/

Contains steady-state simulation data:

  • raw_data.pth: Raw steady-state simulation results

2. transient_data/ (~3GB for each case)

Contains 730 individual case folders (stable_*) with:

  • shape.obj: Original aneurysm geometry mesh
  • shape_remeshed.obj: Remeshed geometry for simulation
  • blood_data.pt: Transient blood flow simulation results (PyTorch tensor)
  • wall_data.pt: Transient Wall Shear Stress (PyTorch tensor)
  • inlet_centroids.csv: 3D coordinates of inlet boundary points (x, y, z)
  • flowsplit_ratio.txt: Flow split ratios at outlets
  • checkpoint.npy: AneuG checkpoints

3. processed_data/ (~32GB)

Pre-processed and assembled data ready for machine learning:

  • assembled_registered_data_1k_v4.pth: Assembled transient flow data
  • assembled_registered_steady_data_1k_v4.pth: Assembled steady-state flow data

4. Geometries.zip

Compressed archive of geometry files.

Licensing Information

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Citation Information

If you wish to use this dataset in your research, please cite the following publication:

[1] Ding, W., Ji, K., Castro, S., Luo, Y., Roi, D., Yap, C.H.: Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025, Lecture Notes in Computer Science 15969, 595--604 (2025).
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