---
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
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  - name: side
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  - name: shape
    dtype: string
  - name: thyroid_volume_ml
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  - name: annotation_gap
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  - name: mask_repaired
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  - name: image
    dtype: image
  - name: mask
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  - name: overlay
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  - name: slice_index
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  - name: n_slices
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  - 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
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  - name: shape
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  - name: thyroid_volume_ml
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  - name: annotation_gap
    dtype: string
  - name: mask_repaired
    dtype: bool
  - name: image
    dtype: image
  - name: mask
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  - name: overlay
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  - name: slice_index
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  - name: n_slices
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  - name: classes_present
    dtype: string
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    num_bytes: 3940848
    num_examples: 28
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- config_name: preview_us
  features:
  - name: case_id
    dtype: string
  - name: subject_id
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  - name: config
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  - name: shape
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  - name: thyroid_volume_ml
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  - name: annotation_gap
    dtype: string
  - name: mask_repaired
    dtype: bool
  - name: image
    dtype: image
  - name: mask
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  - 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".*