Datasets:
File size: 2,832 Bytes
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license: cc-by-3.0
task_categories:
- image-segmentation
tags:
- medical
- ct
- thoracic
- lung
- radiotherapy
- oar
- dicom
- rtstruct
size_categories:
- n<1K
dataset_info:
features:
- name: patient_id
dtype: string
- name: institution
dtype: string
- name: num_slices
dtype: int32
- name: middle_z
dtype: int32
- name: classes_present
list: string
- name: middle_slice
dtype: image
- name: middle_mask
dtype: image
- name: middle_overlay
dtype: image
splits:
- name: train
num_bytes: 8692471
num_examples: 36
- name: test_offsite
num_bytes: 3015476
num_examples: 12
- name: test_online
num_bytes: 2868798
num_examples: 12
download_size: 14561146
dataset_size: 14576745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test_offsite
path: data/test_offsite-*
- split: test_online
path: data/test_online-*
---
# LCTSC — Lung CT Segmentation Challenge 2017 (AAPM)
Mirror of the [TCIA LCTSC collection](https://www.cancerimagingarchive.net/collection/lctsc/) for the AAPM 2017 Thoracic Auto-segmentation Grand Challenge. 60 thoracic CT cases (CT volumes + DICOM RTSTRUCT contours) covering five organs at risk for radiation treatment planning.
## Classes (5 OARs)
| Class | Notes |
| --- | --- |
| Esophagus | |
| Heart | |
| Left Lung | RTSTRUCT splits Lungs into L/R |
| Right Lung | |
| Spinal Cord | |
## Splits
| Split | Cases | HF folder |
| --- | --- | --- |
| Training | 36 | `train/` |
| Off-site test | 12 | `test_offsite/` |
| Online (live) test | 12 | `test_online/` |
| **Total** | **60** | |
Cases are stratified across three institutions (S1: MD Anderson, S2: MSKCC, S3: MAASTRO) — case IDs encode the institution: e.g., `LCTSC-Train-S2-005`.
## Data layout
Each patient folder contains:
- one CT series (`<study_uid>/<series_uid>/*.dcm`, ~140 slices, 512×512, ~512 KB/slice)
- one DICOM RTSTRUCT (`<study_uid>/<small_uid>/1-1.dcm`, ~2 MB) with the five OAR contours
```
train/LCTSC-Train-S1-001/<study>/<ct_series>/*.dcm
train/LCTSC-Train-S1-001/<study>/<rtstruct_series>/*.dcm
```
## License & citation
Released under **CC BY 3.0** by TCIA.
When using this data, cite the original publication and TCIA per [the LCTSC citation policy](https://www.cancerimagingarchive.net/collection/lctsc/):
> Yang J, Veeraraghavan H, Armato SG III, et al. Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med Phys. 2018;45(10):4568-4581. doi:10.1002/mp.13141
> Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging. 2013;26(6):1045-1057.
Original DOI: [10.7937/K9/TCIA.2017.3R3FVZ08](https://doi.org/10.7937/K9/TCIA.2017.3R3FVZ08).
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