Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases

Feng Yang, Pu Xuan Lu, Min Deng, Yì Xiáng J. Wáng, Sivaramakrishnan Rajaraman, Zhiyun Xue, Les R. Folio, Sameer K. Antani and Stefan Jaeger
Additional contact information
Feng Yang: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Pu Xuan Lu: Department of Radiology, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
Min Deng: Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, N.T., Hong Kong
Yì Xiáng J. Wáng: Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, N.T., Hong Kong
Sivaramakrishnan Rajaraman: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Zhiyun Xue: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Les R. Folio: Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL 33612, USA
Sameer K. Antani: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Stefan Jaeger: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA

Data, 2022, vol. 7, issue 7, 1-5

Abstract: Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine-region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People’s Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state of the art for image segmentation methods toward improving the performance of the fine-grained segmentation of TB-consistent findings in digital chest X-ray images. The annotation collection comprises the following: (1) annotation files in JavaScript Object Notation (JSON) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; (2) mask files saved in PNG format for each abnormality per TB patient; and (3) a comma-separated values (CSV) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs.

Keywords: tuberculosis (TB); annotations; abnormalities; computer-aided diagnosis; chest X-ray (CXR) images (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/7/7/95/pdf (application/pdf)
https://www.mdpi.com/2306-5729/7/7/95/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:7:y:2022:i:7:p:95-:d:861538

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().