README.md

metadata

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 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:

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.