Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators

Diedre S. Carmo; Alejandro A. Pezzulo; Raul A. Villacreses; McKenna L. Eisenbeisz; Rachel L. Anderson; Sarah E Van Dorin; Letícia Rittner; Roberto A. Lotufo; Sarah E. Gerard; Joseph M. Reinhardt; Alejandro P. Comellas

doi:10.1038/s41597-025-04709-2

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Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators

Journal article

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Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators

Diedre S. Carmo, Alejandro A. Pezzulo, Raul A. Villacreses, McKenna L. Eisenbeisz, Rachel L. Anderson, Sarah E Van Dorin, Letícia Rittner, Roberto A. Lotufo, Sarah E. Gerard, Joseph M. Reinhardt, …

Scientific data, Vol.12(1), 402

03/07/2025

DOI: 10.1038/s41597-025-04709-2

PMCID: PMC11889079

PMID: 40055348

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Abstract

The field of supervised automated medical imaging segmentation suffers from relatively small datasets with ground truth labels. This is especially true for challenging segmentation problems that target structures with low contrast and ambiguous boundaries, such as ground glass opacities and consolidation in chest computed tomography images. In this work, we make available the first public dataset of ground glass opacity and consolidation in the lungs of Long COVID patients. The Long COVID Iowa-UNICAMP dataset (LongCIU) was built by three independent expert annotators, blindly segmenting the same 90 selected axial slices manually, without using any automated initialization. The public dataset includes the final consensus segmentation in addition to the individual segmentation from each annotator (360 slices total). This dataset is a valuable resource for training and validating new automated segmentation methods and for studying interrater uncertainty in the segmentation of lung opacities in computed tomography.

639/166/985

639/705/1046

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Humanities and Social Sciences

multidisciplinary

Science

Science (multidisciplinary)

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Title: Subtitle: Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators
Creators: Diedre S. Carmo - Universidade Estadual de Campinas (UNICAMP)
Alejandro A. Pezzulo - University of Iowa
Raul A. Villacreses - University of Iowa
McKenna L. Eisenbeisz - University of Iowa
Rachel L. Anderson - University of Iowa
Sarah E Van Dorin - University of Iowa Hospitals and Clinics
Letícia Rittner - Universidade Estadual de Campinas (UNICAMP)
Roberto A. Lotufo - Universidade Estadual de Campinas (UNICAMP)
Sarah E. Gerard - University of Iowa
Joseph M. Reinhardt - University of Iowa
Alejandro P. Comellas - University of Iowa
Resource Type: Journal article
Publication Details: Scientific data, Vol.12(1), 402
DOI: 10.1038/s41597-025-04709-2
PMID: 40055348
PMCID: PMC11889079
NLM abbreviation: Sci Data
eISSN: 2052-4463
Publisher: Nature Publishing Group UK
Grant note: 2019/21964-4; 2022/02344-8 / Fundação de Amparo à Pesquisa do Estado de São Paulo (São Paulo Research Foundation) (501100001807) 317133/2023-3; 313047/2022-7 / Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development) (501100003593) 506728/2020-00 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education) (501100002322)
Language: English
Date published: 03/07/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; Health Management and Policy; ICTS; Iowa Neuroscience Institute; Internal Medicine
Record Identifier: 9984797930402771

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