@article{940e3521a1df445897b0cd8187cb6fc0,
title = "PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines",
abstract = "This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive “NSCLC Radiomics” data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs — where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from “NSCLC Radiomics,” pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.",
keywords = "computer-aided decision support systems, image processing, image segmentation techniques, informatics in imaging, quantitative imaging",
author = "Kiser, {Kendall J.} and Sara Ahmed and Sonja Stieb and Mohamed, {Abdallah S.R.} and Hesham Elhalawani and Park, {Peter Y.S.} and Doyle, {Nathan S.} and Wang, {Brandon J.} and Arko Barman and Zhao Li and Zheng, {W. Jim} and Fuller, {Clifton D.} and Luca Giancardo",
note = "Funding Information: is funded by a grant from the Swiss Cancer League (BIL KLS‐4300‐08‐2017). has received funding and salary support unrelated to this project from: the National Institutes of Health (NIH) National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and an Academic Industrial Partnership Grant (R01DE028290); National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image‐Guided Interventions Program (1R01CA218148); an NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672) and an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50CA097007); National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679); NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) standard grant (NSF 1933369) a National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787‐01); the NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825). has also received direct industry grant support, honoraria, and travel funding from Elekta AB. and are supported in part by the NIH (UL1TR003167) and the Cancer Prevention and Research Institute of Texas (RP 170668). is also supported by an NIH grant (R01AG066749), and is also supported by a Learning Healthcare Award funded by the UTHealth Center for Clinical and Translational Science (CCTS). SMS CDF CDF WJZ LG WJZ LG Funding Information: SMS is funded by a grant from the Swiss Cancer League (BIL KLS-4300-08-2017). CDF has received funding and salary support unrelated to this project from: the National Institutes of Health (NIH) National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and an Academic Industrial Partnership Grant (R01DE028290); National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program (1R01CA218148); an NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672) and an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50CA097007); National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679); NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) standard grant (NSF 1933369) a National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787-01); the NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825). CDF has also received direct industry grant support, honoraria, and travel funding from Elekta AB. WJZ and LG are supported in part by the NIH (UL1TR003167) and the Cancer Prevention and Research Institute of Texas (RP 170668). WJZ is also supported by an NIH grant (R01AG066749), and LG is also supported by a Learning Healthcare Award funded by the UTHealth Center for Clinical and Translational Science (CCTS). Publisher Copyright: {\textcopyright} 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine",
year = "2020",
month = nov,
doi = "10.1002/mp.14424",
language = "English (US)",
volume = "47",
pages = "5941--5952",
journal = "Medical physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "11",
}