---
license: cc-by-4.0
task_categories:
- image-segmentation
modality:
- CT
language: []
tags:
- medical-imaging
- liver-tumor-segmentation
- hepatocellular-carcinoma
- HCC
- multiphase-CT
- TACE
- abdominal-CT
pretty_name: WAW-TACE
size_categories:
- n<1K
dataset_info:
  features:
  - name: sample_id
    dtype: string
  - name: patient_id
    dtype: int32
  - name: phase
    dtype: int32
  - name: phase_name
    dtype: string
  - name: num_slices
    dtype: int32
  - name: num_tumors
    dtype: int32
  - name: ct_middle_slice
    dtype: image
  - name: tumor_mask_middle_slice
    dtype: image
  - name: tumor_overlay_middle_slice
    dtype: image
  splits:
  - name: train
    num_bytes: 230428765
    num_examples: 854
  download_size: 230443173
  dataset_size: 230428765
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# WAW-TACE — Warsaw TACE HCC Multiphase CT Dataset

A multiphase abdominal CT dataset of 233 treatment-naive hepatocellular
carcinoma (HCC) patients receiving trans-arterial chemo-embolization (TACE)
monotherapy at the Medical University of Warsaw, with 377 hand-crafted liver
tumor masks plus auto-generated whole-abdomen organ masks, radiomics
features, and clinical outcomes.

## Dataset Summary

| Field | Details |
|---|---|
| Modality | Multiphase abdominal CT (NIfTI) |
| Body Part | Liver (HCC tumors) + 104 abdominal organ VOIs |
| Subjects | 233 treatment-naive HCC patients (TACE monotherapy) |
| CT volumes | 854 (precontrast=200, arterial=230, portal_venous=231, delayed=193) |
| Tumor masks | 378 hand-crafted, expert-validated (gold) |
| Organ masks | 854 TotalSegmentator-generated, no manual review (silver) |
| License | CC-BY-4.0 |
| Source | https://zenodo.org/records/12741586 |

## Phases

CT and mask filenames encode the phase as an integer suffix:

| ID | Phase |
|---|---|
| 0 | Precontrast / native |
| 1 | Late arterial |
| 2 | Portal venous |
| 3 | Delayed |

Not every patient has every phase. See `metadata/ct_hcc_metadata_v2.csv` for
per-patient phase availability.

## Data Structure

```
WAW-TACE/
├── README.md
├── images/
│   └── {patient_id}_{phase}.nii.gz             # 854 CT volumes
├── tumor_masks/
│   └── {patient_id}_{phase}_{tumor_idx}.nrrd   # 378 hand-crafted tumor masks
├── organ_masks/
│   └── {patient_id}_{phase}.nii.gz             # 854 TotalSegmentator masks (104 VOIs)
└── metadata/
    ├── clinical_data_wawtace_v2_15_07_2024.xlsx
    ├── ct_hcc_metadata_v2.csv
    ├── radiomics_data_wawtace_09_05_2024.xlsx
    └── supplementary_table_s1_definitions_v2.xlsx
```

`{tumor_idx}` indexes multiple tumors annotated on the same CT (e.g. patient
102, phase 1 has six tumor masks: `102_1_0.nrrd` … `102_1_5.nrrd`). Each tumor
mask is a binary 3D NRRD aligned to the corresponding `images/{patient}_{phase}.nii.gz`.

## Mask Provenance — Recommended Ground Truth

**Tumor masks (gold).** TotalSegmentator produced initial tumor guesses;
the primary radiologist (K.B., 6 yrs experience, 2 yrs in segmentation)
manually corrected each in 3D Slicer; two senior radiologists
(K.K. — 14 yrs, K.L. — 11 yrs in TACE/abdominal radiology) independently
validated and modified the corrections; final 3 mm Gaussian smoothing was
applied. Use these for tumor segmentation benchmarks.

**Organ masks (silver).** 104 abdominal VOIs (liver, spleen, kidneys, etc.)
produced per CT phase by TotalSegmentator (nnU-Net pretrained). The original
paper extracted radiomics features from these *uncorrected* masks. Treat as
weak / auxiliary labels, not gold.

## Splits

The released dataset has **no official train/val/test split**. All 233
patients form a single pool — define your own split downstream.

## Citation

```bibtex
@article{bartnik2024wawtace,
  title   = {WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset
             with Segmentations, Radiomics Features, and Clinical Data},
  author  = {Bartnik, Krzysztof and Bartczak, Tomasz and Krzyzi{\'n}ski, Mateusz
             and Korzeniowski, Krzysztof and Lamparski, Krzysztof and W{\k{e}}grzyn, Piotr
             and Lam, Eric and Bartkowiak, Magdalena and Wr{\'o}blewski, Tadeusz
             and Mech, Krzysztof and Januszewicz, Magdalena and Biecek, Przemys{\l}aw},
  journal = {Radiology: Artificial Intelligence},
  year    = {2024},
  doi     = {10.1148/ryai.240296},
  pmid    = {39441110}
}
```

## Sources

- Zenodo: https://zenodo.org/records/12741586
- Paper: https://pubs.rsna.org/doi/full/10.1148/ryai.240296
- PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC11605144/