PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

Year: 2020

Type: article

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.

Source: Medical Physics

DOI: 10.1002/mp.14424

Authors: Kendall J. Kiser, Sara Ahmed, Sonja Stieb, Abdallah S. R. Mohamed, Hesham Elhalawani, Peter Y. S. Park, Nathan S. Doyle, Brandon J. Wang, Arko Barman, Zhao Li, W. Jim Zheng, Clifton D. Fuller, Luca Giancardo

Institutions: The University of Texas MD Anderson Cancer Center, The University of Texas Health Science Center at Houston, Cleveland Clinic

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,700

Funders: National Science Foundation, Cancer Prevention and Research Institute of Texas, Krebsliga Schweiz, National Institutes of Health, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, National Institute on Aging, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research, National Center for Advancing Translational Sciences

OpenAlex ID: W3047489308