@article{820dfe3e9e35434d98db00260588cf72,
title = "CT images with expert manual contours of thoracic cancer for benchmarking auto-segmentation accuracy",
abstract = "Purpose: Automatic segmentation offers many benefits for radiotherapy treatment planning; however, the lack of publicly available benchmark datasets limits the clinical use of automatic segmentation. In this work, we present a well-curated computed tomography (CT) dataset of high-quality manually drawn contours from patients with thoracic cancer that can be used to evaluate the accuracy of thoracic normal tissue auto-segmentation systems. Acquisition and validation methods: Computed tomography scans of 60 patients undergoing treatment simulation for thoracic radiotherapy were acquired from three institutions: MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, and the MAASTRO clinic. Each institution provided CT scans from 20 patients, including mean intensity projection four-dimensional CT (4D CT), exhale phase (4D CT), or free-breathing CT scans depending on their clinical practice. All CT scans covered the entire thoracic region with a 50-cm field of view and slice spacing of 1, 2.5, or 3 mm. Manual contours of left/right lungs, esophagus, heart, and spinal cord were retrieved from the clinical treatment plans. These contours were checked for quality and edited if necessary to ensure adherence to RTOG 1106 contouring guidelines. Data format and usage notes: The CT images and RTSTRUCT files are available in DICOM format. The regions of interest were named according to the nomenclature recommended by American Association of Physicists in Medicine Task Group 263 as Lung_L, Lung_R, Esophagus, Heart, and SpinalCord. This dataset is available on The Cancer Imaging Archive (funded by the National Cancer Institute) under Lung CT Segmentation Challenge 2017 (http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08). Potential applications: This dataset provides CT scans with well-delineated manually drawn contours from patients with thoracic cancer that can be used to evaluate auto-segmentation systems. Additional anatomies could be supplied in the future to enhance the existing library of contours.",
keywords = "CT, automatic segmentation, grand challenge, radiation therapy, thoracic cancer",
author = "Jinzhong Yang and Harini Veeraraghavan and {van Elmpt}, Wouter and Andre Dekker and Mark Gooding and Greg Sharp",
note = "Funding Information: The authors thank the American Association of Physicists in Medicine (AAPM) for supporting and sponsoring the 2017 AAPM Thoracic Auto‐segmentation Challenge and The Cancer Imaging Archive (TCIA) (funded by the National Cancer Institute) for hosting the datasets and making them available to the public. We further thank Artem Mamonov and Andrew Beers from Harvard Medical School for providing valuable support for the challenge website, Kirk Smith and Tracy Nolan from the University of Arkansas for Medical Sciences for data curation to TCIA, Tim Lustberg from Maastro Clinic for data collection, and Christine Wogan from MD Anderson Cancer Center for reviewing the manuscript. Dr. Yang is supported in part by the National Institutes of Health Cancer Center Support (Core) Grant no. P30 CA016672 to The University of Texas MD Anderson Cancer Center. Dr. Veeraraghavan is supported in part by the National Institutes of Health Cancer Center Support (Core) Grant P30 CA008748 (to the institution). Dr. Dekker acknowledges support from National Institutes of Health grant no. U01 CA143062 (Radiomics of NSCLC). Funding Information: The authors thank the American Association of Physicists in Medicine (AAPM) for supporting and sponsoring the 2017 AAPM Thoracic Auto-segmentation Challenge and The Cancer Imaging Archive (TCIA) (funded by the National Cancer Institute) for hosting the datasets and making them available to the public. We further thank Artem Mamonov and Andrew Beers from Harvard Medical School for providing valuable support for the challenge website, Kirk Smith and Tracy Nolan from the University of Arkansas for Medical Sciences for data curation to TCIA, Tim Lustberg from Maastro Clinic for data collection, and Christine Wogan from MD Anderson Cancer Center for reviewing the manuscript. Dr. Yang is supported in part by the National Institutes of Health Cancer Center Support (Core) Grant no. P30 CA016672 to The University of Texas MD Anderson Cancer Center. Dr. Veeraraghavan is supported in part by the National Institutes of Health Cancer Center Support (Core) Grant P30 CA008748 (to the institution). Dr. Dekker acknowledges support from National Institutes of Health grant no. U01 CA143062 (Radiomics of NSCLC). Publisher Copyright: {\textcopyright} 2020 American Association of Physicists in Medicine",
year = "2020",
month = jul,
day = "1",
doi = "10.1002/mp.14107",
language = "English (US)",
volume = "47",
pages = "3250--3255",
journal = "Medical physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "7",
}