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

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

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04709-2

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https://doi.org/10.1038/s41597-025-04709-2

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