Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism

Autor: João Mario Clementin de Andrade, Gabriel Olescki, Dante Luiz Escuissato, Lucas Ferrari Oliveira, Ana Carolina Nicolleti Basso, Gabriel Lucca Salvador
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Data, Vol 10, Iss 1, Pp 1-8 (2023)
Druh dokumentu: article
ISSN: 2052-4463
10533133
DOI: 10.1038/s41597-023-02374-x
Popis: Abstract Pulmonary embolism has a high incidence and mortality, especially if undiagnosed. The examination of choice for diagnosing the disease is computed tomography pulmonary angiography. As many factors can lead to misinterpretations and diagnostic errors, different groups are utilizing deep learning methods to help improve this process. The diagnostic accuracy of these methods tends to increase by augmenting the training dataset. Deep learning methods can potentially benefit from the use of images acquired with devices from different vendors. To the best of our knowledge, we have developed the first public dataset annotated at the pixel and image levels and the first pixel-level annotated dataset to contain examinations performed with equipment from Toshiba and GE. This dataset includes 40 examinations, half performed with each piece of equipment, representing samples from two medical services. We also included measurements related to the cardiac and circulatory consequences of pulmonary embolism. We encourage the use of this dataset to develop, evaluate and compare the performance of new AI algorithms designed to diagnose PE.
Databáze: Directory of Open Access Journals
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