Datasets:
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
@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}
}