| { |
| "ops_run": "bench-fleet D1 NIfTI conversion (nucleus_instance_3d)", |
| "purpose": "NIfTI (.nii.gz) copies of this repo's image and segmentation volumes, added ALONGSIDE the originals (nothing deleted or overwritten), so the MedOtter scoring engine -- which reads NIfTI only, via nibabel -- can score the nucleus_instance_3d benchmark cohort.", |
| "date": "2026-09-08", |
| "tools": { |
| "python": "3.12.3", |
| "numpy": "2.5.3", |
| "nibabel": "5.4.2", |
| "h5py": "3.16.0", |
| "tifffile": "2026.8.23" |
| }, |
| "conventions": { |
| "axis_order": "(Z, Y, X): the source page/voxel order is written unchanged. medotter_core.inference's VolumeLoader / InstanceVolumeLoader read np.asarray(img.dataobj) and do NOT reorient, so they see exactly the source array.", |
| "affine": "identity (np.eye(4)), zooms (1, 1, 1). Neither source format carries a machine-readable voxel size (NIS3D states resolution as prose in Info.txt; the NucMM-Z HDF5 files carry none) and this benchmark's metrics (f1 / aji / pq over voxel IoU) consume no spacing, so no spacing is invented.", |
| "values": "preserved exactly: every written file was reloaded and asserted np.array_equal against the source array (and, for segmentations, against the int32 cast the instance loader applies).", |
| "dtypes": "images keep their stored dtype; segmentations use the smallest integer dtype that holds the cohort's maximum instance id, capped at int32 because InstanceVolumeLoader.load_gt casts to int32. No relabelling: instance ids are byte-identical." |
| }, |
| "license": { |
| "dataset_card": "medotter/knowledge/datasets/NIS3D.data.final.yaml", |
| "license": "CC BY 4.0", |
| "redistribution_allowed": "yes", |
| "verdict": "Zenodo record 11456029 (NIS3D, NeurIPS 2023 D&B) is CC BY 4.0, which permits redistribution with attribution; the card's redistribution.allowed is 'yes'.", |
| "origin_url": "https://zenodo.org/records/11456029" |
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