Upload Graz aortic dissection CTA + true/false lumen masks (binarised, case-40 masks resampled to image grid, merged map added)

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+tags:
+- medical
+- cta
+- ct
+- aorta
+- aortic-dissection
+- vascular
+- 3d
+pretty_name: Aortic Dissection (Graz) - true/false lumen CTA
+size_categories:
+- n<1K
+---
+
+# Aortic Dissection (Graz) - CTA with true / false lumen expert annotations
+
+Re-host of **"Aortic Dissection Dataset and Segmentations"**
+([figshare 10.6084/m9.figshare.22269091](https://doi.org/10.6084/m9.figshare.22269091),
+CC BY 4.0) - 40 **type-B aortic dissection** CTA volumes collected in clinical
+routine 2005-2021 across the Medical University of Graz regional hospital
+network, with expert **true-lumen** and **false-lumen** annotations.
+
+> Mayer C, Pepe A, Hossain S, Karner B, Arnreiter M, Kleesiek J, Schmid J,
+> Janisch M, Fuchsjaeger M, Deutschmann H, Zimpfer D, Egger J, Maechler H.
+> *Type B Aortic Dissection CTA Collection with True and False Lumen Expert
+> Annotations for the Development of AI-based Algorithms.*
+> **Scientific Data 11, 596 (2024).** doi:10.1038/s41597-024-03284-2
+
+Ethics: Medical University of Graz EK-34-161 ex 21/22; consent waived
+(retrospective), head and face cropped for anonymisation.
+
+## Contents
+
+| | |
+|---|---|
+| Modality | CTA, arterial phase (**not** ECG-gated) |
+| Cases | 40, one volume each |
+| Classes | `0` background, `1` true lumen, `2` false lumen |
+| Split | **none** - single cohort of 40 (`split="train"` for all rows) |
+| Shape | mostly 512x512xZ; in-plane spacing 0.525-0.965 mm, through-plane 1.25-3.0 mm |
+| Foreground | TL 125,516-659,055 voxels; FL 25,242-496,428 voxels; **no empty masks** |
+
+**There is no thrombus class.** Per the paper, "clearly thrombosed sections have
+not been segmented"; calcifications and atherosclerotic changes are likewise
+excluded. Ambiguous dark regions judged to be late-filling artifact were
+segmented *as lumen*. If you need a thrombus label, see ImageTBAD instead.
+
+## Layout
+
+```
+dataset/ctaNNs/ctaNNs.nrrd           image, int16 HU - byte-identical to figshare
+               truelumenNN.seg.nrrd  true lumen,  uint8 {0,1}
+               falselumenNN.seg.nrrd false lumen, uint8 {0,1}
+               maskNN.nrrd           merged {0=bg, 1=TL, 2=FL}
+               meshNN.stl            surface mesh - byte-identical to figshare
+original_masks/ctaNNs/*.seg.nrrd     untouched source masks (~13 MB total)
+train.jsonl                          40 rows (schema below)
+```
+
+`train.jsonl` per row: `case_id`, `case_num`, `image`, `mask`,
+`mask_true_lumen`, `mask_false_lumen`, `mesh`, `original_mask_*`, `shape`,
+`spacing_mm`, `coarse_axis`, `tl_voxels`, `fl_voxels`, `tl_volume_ml`,
+`fl_volume_ml`, `tl_fl_overlap_voxels`, `fl_label_value_raw`,
+`resampled_to_image_grid`, `annotator_tier`, `split`.
+
+## What was normalised here (and why)
+
+Everything below is flagged per-case in `train.jsonl`, and the untouched source
+masks ship under `original_masks/`.
+
+1. **False-lumen masks binarised to `{0,1}`.** 17 of 40 cases store the false
+   lumen with **label value 2**, the other 23 with 1 - cases
+   `4, 5, 11, 12, 13, 14, 15, 16, 17, 18, 22, 25, 26, 30, 35, 36, 38`. Code
+   doing `mask == 1` against the raw files silently drops those 17 false
+   lumens. Original value kept in `fl_label_value_raw`.
+2. **Case 40 masks resampled onto the image grid.** As shipped, case 40's masks
+   sit on a grid rotated ~6.85 deg about Z (512x1103x187 @0.545 mm) while its
+   image is axis-aligned (512x512x401 @0.602 mm) - index-wise pairing raises
+   `IndexError`. Nearest-neighbour world-space resampling retains **100.01% /
+   100.04%** of physical volume, and mean HU inside the resampled masks is
+   **409 HU (TL) / 339 HU (FL)**, i.e. contrast-filled arterial lumen and in
+   line with the other 39 cases. Flagged by `resampled_to_image_grid`.
+   Every other case's masks already match their image exactly.
+3. **Merged `maskNN.nrrd` added**, painted TL first then FL, so **FL wins
+   ties**. Ties are negligible: 16 of 40 cases overlap by 1-106 voxels, at most
+   **0.028%** of the TL-or-FL union - flap-boundary jitter, not a nested
+   structure. The two binaries remain available if you prefer to fan out.
+
+Deliberately **not** changed: no reorientation, no resampling of the other 39
+cases, no intensity windowing, original filenames kept.
+
+## Gotchas
+
+- **Key masks off the FILENAME, never the segment name.** Internal Slicer names
+  are inconsistent: case 2's true lumen is called `artery`, case 19's *false*
+  lumen is also called `artery`, and elsewhere you find `true lumen 5`,
+  `true_lumen`, `truelumen`, `True Lumen neu`, `flase lumen`, `False_Lumen`.
+- **Cases 19 and 20 are sagittally reformatted**: the coarse 1.5 mm axis is
+  NRRD axis 0, not axis 2 (all other 38 cases have it at axis 2). A hard-coded
+  z-slicer emits ~950 thin reformats instead of ~240 native slices. Use
+  `coarse_axis` from `train.jsonl`.
+- **No official split.** Group any split you make on `case_id`; one patient owns
+  exactly one volume, so a plain case-level split is safe.
+- **Anisotropic**, up to 3.0 mm through-plane.
+- The images are the anonymised, head/face-cropped reconstructions. The
+  separate `raw CTs.zip` on figshare is **not** mirrored here on purpose: its
+  case 24 is a *different reconstruction* (776x776x101 oblique @0.625/0.625/2.4
+  mm) that does **not** align with the masks.
+
+## Annotation provenance - one tier, all of it gold
+
+All 40 masks were seeded semi-automatically (local thresholding / Grow Cut,
+fill-between-slices interpolation, or region growing - the paper's Table 2 gives
+the per-case method), then given **2-3 hours of manual slice-by-slice
+paint/erase**, because the paper reports the semi-automated results "were not
+precise enough for AI applications". A radiologist then checked every case.
+
+`annotator_tier` records who did the manual work: 15 cases (`1-10, 26, 27, 28,
+29, 39`, the paper's Table-1 nabla marker) by a medical student, the other 25 by
+cardiac-surgery residents. The paper marks these deliberately *to show there is
+no difference between software users* - so **all 40 are gold and none should be
+filtered**; the column is there for stratification only.
+
+**Measured vs published volumes.** The `tl_volume_ml` / `fl_volume_ml` values
+here are measured directly from the mask files. They reproduce the paper's
+Table 2 to within +/-1% for 35 of 40 cases (most within 0.1%), which
+independently confirms the image-mask pairing and the binarisation. Five cases
+(`5, 6, 7, 32, 36`) disagree in the **true-lumen column only**; in three of them
+the paper's TL cell exactly equals its FL cell, which looks like a transcription
+slip. Each mask file contains exactly one correctly-named segment, so this is a
+table artifact, not extra structures in the data. Trust the values here.
+
+## Related datasets / leakage
+
+**No patient overlap with [`MedOtter/SegA`](https://huggingface.co/datasets/MedOtter/SegA)**
+(the AVT collection: KiTS 20 + RIDER 18 + Dongyang 18 - US and Chinese cohorts).
+The two share *authors* (Pepe, Egger), not patients, and AVT's few incidental
+dissection cases carry only a single binary aortic-vessel-tree mask, not
+lumen labels. No overlap with AortaSeg24 (Univ. of Florida), ImageTBAD
+(Guangdong), TotalSegmentator (Basel), or any TCIA collection. No
+cross-reference ID to other datasets exists; `case_id` (`cta01`-`cta40`) joins
+only to Tables 1 and 2 of the paper.
+
+## License
+
+**CC BY 4.0** - redistribution and commercial use permitted with attribution.
+Cite the Scientific Data paper above and the figshare record.

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