Dataset Open Access

LoDoPaB-CT Challenge Set

Leuschner, Johannes; Schmidt, Maximilian; Otero Baguer, Daniel

Observation data for the LoDoPaB-CT challenge, which asks to reconstruct CT images of the human lung from (simulated) low photon count measurements.

The setting is identical to the one of the LoDoPaB-CT dataset (documented in an arXiv preprint), which is supposed to be employed for training learned methods. This challenge set contains observations for a separate set of patients.

Python utilities for accessing this challenge set and creating the submission file are available at github.com/jleuschn/lodopab_challenge. The LoDoPaB-CT dataset for training can be accessed using the DIVal python library (github.com/jleuschn/dival).

Like for the LoDoPaB-CT dataset, reconstructions from the LIDC/IDRI dataset are used as a basis for this challenge set.

 

The ZIP file contains multiple HDF5 files. Each HDF5 file contains one HDF5 dataset named "data", that provides a number of samples (128 except for the last file). For example, the n-th observation sample is stored in the file "observation_challenge_%03d.hdf5" where "%03d" is floor(n / 128), at row (n mod 128) of "data".

For this challenge set no patient IDs are provided (in contrast to the fully public parts of the LoDoPaB-CT dataset), since the reconstruction algorithm should not rely on this information.

Files (3.1 GB)
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observation_challenge.zip
md5:b2b200933f7e2a39a216e1d3bba1880d
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