| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - ultrasound |
| - 3d-ultrasound |
| - mri |
| - thyroid |
| - neck |
| - carotid-artery |
| - jugular-vein |
| - vessel-segmentation |
| - segthy |
| - multimodal |
| pretty_name: SegThy - Thyroid and Neck Vessel Segmentation (3D Ultrasound + MRI) |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: preview_mri_multiclass |
| data_files: |
| - split: train |
| path: preview_mri_multiclass/train-* |
| - config_name: preview_mri_thyroid |
| data_files: |
| - split: train |
| path: preview_mri_thyroid/train-* |
| - config_name: preview_us |
| data_files: |
| - split: train |
| path: preview_us/train-* |
| dataset_info: |
| - config_name: preview_mri_multiclass |
| features: |
| - name: case_id |
| dtype: string |
| - name: subject_id |
| dtype: string |
| - name: config |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: side |
| dtype: string |
| - name: shape |
| dtype: string |
| - name: thyroid_volume_ml |
| dtype: float32 |
| - name: annotation_gap |
| dtype: string |
| - name: mask_repaired |
| dtype: bool |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: slice_index |
| dtype: int32 |
| - name: n_slices |
| dtype: int32 |
| - name: classes_present |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 1979055 |
| num_examples: 14 |
| download_size: 1989056 |
| dataset_size: 1979055 |
| - config_name: preview_mri_thyroid |
| features: |
| - name: case_id |
| dtype: string |
| - name: subject_id |
| dtype: string |
| - name: config |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: side |
| dtype: string |
| - name: shape |
| dtype: string |
| - name: thyroid_volume_ml |
| dtype: float32 |
| - name: annotation_gap |
| dtype: string |
| - name: mask_repaired |
| dtype: bool |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: slice_index |
| dtype: int32 |
| - name: n_slices |
| dtype: int32 |
| - name: classes_present |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 3940848 |
| num_examples: 28 |
| download_size: 3950988 |
| dataset_size: 3940848 |
| - config_name: preview_us |
| features: |
| - name: case_id |
| dtype: string |
| - name: subject_id |
| dtype: string |
| - name: config |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: side |
| dtype: string |
| - name: shape |
| dtype: string |
| - name: thyroid_volume_ml |
| dtype: float32 |
| - name: annotation_gap |
| dtype: string |
| - name: mask_repaired |
| dtype: bool |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: slice_index |
| dtype: int32 |
| - name: n_slices |
| dtype: int32 |
| - name: classes_present |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 5853237 |
| num_examples: 32 |
| download_size: 5869481 |
| dataset_size: 5853237 |
| --- |
| |
| # SegThy — Thyroid and Neck Vessel Segmentation (3-D Ultrasound + MRI) |
|
|
| **SegThy** pairs **electromagnetically tracked freehand 3-D ultrasound** with |
| **T1-VIBE MRI** of the same healthy volunteers, and annotates the **thyroid gland, |
| both common carotid arteries and both internal jugular veins** by hand in each. |
|
|
| Two things make it unusual. It is one of very few public datasets with **3-D |
| reconstructed freehand ultrasound** rather than 2-D B-mode frames — the sweeps are |
| tracked and resampled to a 0.12 mm isotropic volume. And **14 subjects carry |
| multi-class labels in both modalities**, which is what makes it a genuine MR/US |
| registration benchmark rather than two unrelated collections. |
|
|
| ## What this mirror contains — read first |
|
|
| > **⚠️ The 504-volume `network_results/` tier is deliberately NOT mirrored.** |
| > It is 94 % of the upstream archive (17.3 GiB) and it is **not ground truth**. |
| > Upstream's own Readme: *"contains the US scans and corresponding thyroid |
| > segmentations **from the trained QuickNAT**"* — these are CNN predictions, and the |
| > object of study in the paper, not a reference standard. Three independent reasons |
| > to exclude them: |
| > 1. They are model output, so evaluating against them measures agreement with |
| > QuickNAT, not with a human. |
| > 2. **Their labels do not overlay their own images.** The label arrays are stored |
| > in the network's resampled frame — `(ceil₄(Z_image), 448, 384)`, axes permuted |
| > with Z first — so shapes disagree with the image in every pair sampled (12/12). |
| > They cannot be used without inverting an undocumented resampling. |
| > 3. **31 of the 32 gold scan IDs reappear inside them** with conflicting labels, so |
| > loading both tiers silently double-counts almost the whole gold set. |
| > |
| > Get them from [the source](https://www.campar.in.tum.de/public_datasets/2022_plosone_eilers/US_data.zip) |
| > if you want them. Everything mirrored here is **100 % manually annotated**. |
|
|
| > **⚠️ The public release is Sub-dataset 1 only.** The project describes two |
| > sub-datasets; only the **28-volunteer** one was ever released. **Sub-dataset 2 |
| > (186 routine-care patients) is not downloadable** and is not here. The homepage |
| > also announces trachea and thyroid-nodule labels as "currently being extended" — |
| > those have never shipped, and the 2022 Readme's promise that the remaining MRI |
| > vessel labels would arrive "in the following weeks" is still outstanding at the |
| > 2025-05-09 file date (14 of 28). **This dataset has no nodules and no pathology**; |
| > the cohort is healthy volunteers. |
|
|
| > **⚠️ Upstream's US Readme says "The label maps are binary". That is false for the |
| > data mirrored here.** The same paragraph also states the vessels *are* manually |
| > segmented, contradicting itself. Verified on all 32 gold masks: left-lobe scans |
| > carry `{0,1,2,3}`, right-lobe scans carry `{0,1,4,5}`. **A loader that assumes |
| > binary will silently relabel every carotid and jugular voxel as thyroid.** The |
| > "binary" sentence describes the excluded `network_results/` tier. |
| |
| ## Dataset Details |
| |
| | Field | Value | |
| |---|---| |
| | Modalities | **Tracked freehand 3-D ultrasound** (Siemens Acuson NX-3, VF12-4 12 MHz, piur tUS) · **T1-weighted VIBE MRI** (Siemens Biograph mMR 3 T) | |
| | Body part | Neck — thyroid gland, common carotid arteries, internal jugular veins | |
| | Cohort | **28 healthy volunteers**, single centre (TUM / Klinikum rechts der Isar) | |
| | Annotation | **100 % manual**, single tier — no rater or tier to choose | |
| | US spacing | **0.12 mm isotropic** (0.11 mm for subjects 006 and 026 — the Readme's flat "0.12" is approximate) | |
| | MRI spacing | **0.625 × 0.625 × 1.0 mm**, all 28 volumes | |
| | US shapes | variable, 457–617 × 363–526 × 359–696 | |
| | MRI shape | **320 × 320 × 80**, all 28 volumes | |
| | Dtypes | US image + mask `uint8` · MRI image `uint16`, masks `uint8` | |
| | Intensity | US **0–247** (as documented) · MRI 0–1666 | |
| | Split | **none upstream** — one `train` split per config | |
| | License | **CC BY**, commercial use explicitly permitted — shipped inside both archives | |
| | Paper | Krönke et al., *PLOS ONE* **17**(7):e0268550, 2022 · [doi:10.1371/journal.pone.0268550](https://doi.org/10.1371/journal.pone.0268550) | |
| |
| ## Label map |
| |
| Identical in both modalities, and confirmed against upstream's MRI Readme |
| (*"the thyroid value being 1, the jugular vein being 3 and 5, the carotid being 2 |
| and 4"*): |
| |
| | Value | Structure | |
| |---|---| |
| | 0 | background | |
| | 1 | thyroid | |
| | 2 | carotid artery, **left** | |
| | 3 | internal jugular vein, **left** | |
| | 4 | carotid artery, **right** | |
| | 5 | internal jugular vein, **right** | |
| |
| **Classes are disjoint** — every voxel carries exactly one label, so this is a single |
| label map and *not* a fan-out into overlapping binary channels. |
| |
| > **⚠️ In the `us` config the class ID depends on which lobe was scanned.** Each US |
| > volume covers one lobe, so a left scan contains only `{0,1,2,3}` and a right scan |
| > only `{0,1,4,5}`. The three missing classes are missing because **that anatomy is |
| > outside the field of view**, not because it was left unannotated — so zero-filling |
| > them is correct here. Contrast this with the MRI configs, where both sides are in |
| > every volume. |
| > |
| > The one genuine annotation gap is **`028_P3_1_left`**, whose alphabet is `{0,1,2}`: |
| > the left jugular is in the field of view but was not drawn, and it is annotated in |
| > all 15 other left scans. It is flagged `annotation_gap` in the jsonl. |
|
|
| ## The three configs |
|
|
| | Config | Volumes | Subjects | Classes | Notes | |
| |---|---|---|---|---| |
| | **`us`** | **32** | 16 | thyroid + vessels of the scanned side | 2 per subject (left + right lobe) | |
| | **`mri_multiclass`** | **14** | 14 | all 5 | the richest tier | |
| | **`mri_thyroid`** | **28** | 28 | thyroid only | widest subject coverage | |
|
|
| ### ⚠️ The two MRI configs are not nested, and their thyroid labels disagree |
|
|
| The 14 `mri_multiclass` subjects are a strict subset of the 28 `mri_thyroid` |
| subjects **and share the same underlying MRI scans** — so **do not evaluate on both |
| and pool the numbers**; 14 subjects would be counted twice. |
|
|
| More surprisingly, for those 14 shared subjects the binary mask is **not** the |
| multiclass thyroid channel. They are two independent delineations: |
|
|
| | | Dice(binary, multiclass==1) | |
| |---|---| |
| | median | **0.912** | |
| | range | **0.750 – 0.999** | |
|
|
| The binary-only voxels land on multiclass *background*, not on vessel labels, so this |
| is not vessels being absorbed into the gland — it is ordinary inter-delineation |
| disagreement, at roughly inter-observer magnitude. |
|
|
| **Which one to trust?** Independent evidence favours `mri_multiclass`. Summing the |
| two US lobe volumes per subject and comparing against the MRI gland volume — the very |
| quantity the paper studies — gives: |
|
|
| | MRI tier | median US-sum / MRI ratio | n | |
| |---|---|---| |
| | **`mri_multiclass`** | **0.947** | 14 | |
| | `mri_thyroid` | 1.264 | 15 | |
| |
| Two independent modalities and annotators agreeing to ~5 % is a strong signal; the |
| binary tier is ~26 % smaller than the same subjects' ultrasound, i.e. it appears to |
| **under-segment**. Use `mri_multiclass` when both are available. **A Dice measured on |
| one MRI config is not comparable to a Dice measured on the other.** |
| |
| ## ⚠️ Subject 005's binary mask is 4-D upstream |
| |
| `005_MRI_thyroid_label.nii.gz` ships as **`(320, 320, 80, 2)`** — the only non-3-D |
| file in the whole release (1 of 134). Volume index **1 is entirely empty** (0 |
| foreground voxels); index 0 holds the real 20,294-voxel mask, whose size is |
| unremarkable among its peers (median 17,453, range 7,251–42,819) and whose Dice |
| against the multiclass thyroid (0.723) sits in the same band as every other subject. |
|
|
| **`np.squeeze` does not fix this** — the trailing axis has length 2, not 1, so it |
| survives the squeeze and breaks any loader that assumes rank 3. |
|
|
| This mirror therefore ships **`[..., 0]`** as the canonical mask, with the affine and |
| header preserved, flagged `mask_repaired: true` in the jsonl. **The untouched |
| original is kept** at `originals/005_MRI_thyroid_label.original_4d.nii.gz`, and both |
| digests are in `metadata/manifest.csv`, so nothing is lost and the change is |
| auditable. Every other file is bit-identical in payload to upstream. |
|
|
| ## Splits, grouping and leakage |
|
|
| **No split ships upstream** — the archives are flat folders with no split file. The |
| paper used an internal 26/6/6 *lobe* split that is not distributed. Rather than |
| invent a boundary, each config ships **one `train` split** and the loader declares a |
| single-split fallback. |
|
|
| > **Group on `subject_id`, never on `case_id`.** A subject contributes up to two `us` |
| > rows (left and right lobe of the same neck, same session) plus an MRI row in each |
| > MRI config. Splitting those across a train/test boundary leaks. `subject_id` is the |
| > 3-digit volunteer number and is **consistent across all three configs and both |
| > modalities** — subject `007` is the same person everywhere. |
| |
| ## Choosing a slicing axis — axis 2 |
| |
| For 2-D slice-wise use, axis 2 carries by far the most annotated slices: |
| |
| | Config | axis 0 | axis 1 | **axis 2** | |
| |---|---|---|---| |
| | `us` | 71.7 % | 39.1 % | **81.4 %** | |
| | `mri_multiclass` | 39.7 % | 17.2 % | **100.0 %** | |
| | `mri_thyroid` | 18.0 % | 13.0 % | **48.1 %** | |
|
|
| (median fraction of slices containing any foreground). `mri_multiclass` reaches 100 % |
| because the carotids and jugulars run the full superior–inferior extent of every |
| volume, while the thyroid alone covers about half of it. |
|
|
| ## Cohort anomalies |
|
|
| - **Subject `029` appears only in the US gold tier** (2 volumes). Every source — |
| homepage, paper, both Readmes — says 28 volunteers and numbers them 001–028. There |
| is no `029` MRI and no `029` anywhere in `network_results`. Its scans are |
| well-formed and normally annotated; it is mirrored as-is and flagged in the |
| cross-reference. |
| - **Subject `004`** has an MRI and a binary thyroid mask but no vessel labels and no |
| US gold data. |
| - `011` and `029` have US gold but no `mri_multiclass`, which is why the |
| both-modalities multi-class intersection is **14** subjects rather than 16. |
|
|
| ## Structure |
|
|
| ``` |
| us/train/images/001_P1_1_left.nii.gz # 32 tracked 3-D US volumes |
| us/train/masks/001_P1_1_left.nii.gz # 32 masks, same grid |
| mri_multiclass/train/images/001.nii.gz # 14 T1-VIBE MRI volumes |
| mri_multiclass/train/masks/001.nii.gz # 14 five-class masks |
| mri_thyroid/train/images/001.nii.gz # 28 T1-VIBE MRI volumes |
| mri_thyroid/train/masks/001.nii.gz # 28 binary thyroid masks |
| |
| us_train.jsonl # per-case metadata, one file per config |
| mri_multiclass_train.jsonl |
| mri_thyroid_train.jsonl |
| |
| metadata/manifest.csv # payload_sha256 + bytes + shape, all files |
| metadata/subject_crossref.csv # which tiers each subject appears in |
| originals/005_MRI_thyroid_label.original_4d.nii.gz # untouched 4-D original |
| |
| Readme_MRI.txt # upstream, verbatim |
| Readme_US.txt # upstream, verbatim |
| README.md |
| LICENSE.txt |
| ``` |
|
|
| > **The `preview_*` configs the Dataset Viewer shows are thumbnails, not the data.** |
| > They hold one rendered PNG triplet (image / colour-coded mask / overlay) per volume, |
| > purely so the collection can be browsed in the Hub UI. **Load the `.nii.gz` files |
| > listed in the jsonl for any real use** — the previews are 8-bit, single-slice and |
| > lossy. |
| |
| US case IDs follow upstream's grammar `NNN_PX_S_side`: subject, physician (`P1`–`P3`), |
| repeat index, lobe. The gold tier holds exactly one scan per subject per lobe, so the |
| physician and repeat fields vary between subjects but never within one. |
| |
| ### jsonl columns |
| |
| | Column | Meaning | |
| |---|---| |
| | `case_id` | unique within the config | |
| | `subject_id` | 3-digit volunteer number — **the grouping key**, consistent across configs | |
| | `config`, `modality` | `us`/`mri_multiclass`/`mri_thyroid`, `US`/`MRI` | |
| | `image`, `mask` | repo-relative paths | |
| | `split` | always `"train"` (no upstream split exists) | |
| | `side`, `physician`, `scan_index` | `us` only, parsed from the filename | |
| | `shape_xyz`, `n_slices`, `spacing_mm` | geometry, read per file | |
| | `axcodes`, `sform_code`, `qform_code` | header provenance | |
| | `image_dtype`, `mask_dtype`, `intensity_min`, `intensity_max` | | |
| | `mask_values` | the label alphabet actually present | |
| | `class_voxels`, `class_volume_ml` | per class, keyed by label ID | |
| | `thyroid_volume_ml` | class 1 volume — clinically the quantity of interest | |
| | `fg_voxels`, `n_voxels`, `foreground_fraction` | | |
| | `fg_slice_fraction`, `leading_bg_slices` | per axis (`"0"`,`"1"`,`"2"`) | |
| | `annotation_gap` | non-null only for `028_P3_1_left` | |
| | `mask_repaired`, `mask_repair_note` | true only for `mri_thyroid` subject 005 | |
| | `has_us_gold`, `has_mri_multiclass`, `has_mri_binary` | cross-tier availability | |
| | `payload_sha256` | digest of the **uncompressed** NIfTI bytes — comparable to upstream regardless of gzip framing | |
| | `image_sha256`, `mask_sha256`, `image_bytes`, `mask_bytes` | as stored here | |
|
|
| ## Overlap and contamination |
|
|
| - **⚠️ SegThy is inside UltraSam's US-43d *training* corpus** (arXiv 2411.16222, |
| listed as `Segthy-Dataset thyroid`). **Benchmarking UltraSam or its derivatives on |
| SegThy is contaminated.** |
| - **No patient overlap with TN3K / TG3K / DDTI / TNSCUI.** Those are 2-D B-mode |
| *nodule* datasets from Chinese and Colombian hospitals; SegThy is 3-D tracked |
| freehand US plus MRI of healthy German volunteers, targeting the gland and vessels. |
| Different target, cohort and acquisition. No cross-reference ID exists or is needed. |
| - **No TCIA, Medical Segmentation Decathlon or BraTS lineage** — single-centre TUM |
| acquisition under TUM Ethics Commission approval. |
| - Clean with respect to MedSAM, MedSAM2, SAM-Med2D and SAM-Med3D. |
| - Reference ceiling: Munsterman et al., *WFUMB Ultrasound Open* **2**:100055 (2024), |
| [doi:10.1016/j.wfumbo.2024.100055](https://doi.org/10.1016/j.wfumbo.2024.100055), |
| trained 2-D/3-D U-Nets on the SegThy tracked sweeps and report median Dice |
| **0.934 / 0.924 / 0.897** for thyroid / carotid / jugular. |
|
|
| ## Source & Citation |
|
|
| - Official, author-hosted: <https://www.cs.cit.tum.de/camp/publications/segthy-dataset/> |
| - Direct: `https://www.campar.in.tum.de/public_datasets/2022_plosone_eilers/` |
| - Avoid the Academic Torrents copy — it carries `US_data.zip` only and its size does |
| not match the current 2025-05-09 revision. |
|
|
| ```bibtex |
| @article{kronke2022segthy, |
| author = {Kr{\"o}nke, Markus and Eilers, Christine and Dimova, Desislava and |
| K{\"o}hler, Melanie and Buschner, Gabriel and Schweiger, Lilit and |
| Konstantinidou, Lemonia and Makowski, Marcus R. and Nagarajah, James |
| and Navab, Nassir and Weber, Wolfgang and Wendler, Thomas}, |
| title = {Tracked 3D ultrasound and deep neural network-based thyroid |
| segmentation reduce interobserver variability in thyroid volumetry}, |
| journal = {PLOS ONE}, |
| volume = {17}, |
| number = {7}, |
| pages = {e0268550}, |
| year = {2022}, |
| doi = {10.1371/journal.pone.0268550} |
| } |
| ``` |
|
|
| *Note: the in-archive Readme credits "Lilit Mirzojan"; PLOS ONE publishes the same |
| author as "Lilit Schweiger".* |
|
|
