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Detecting tuberculosis in radiographs using combined lung masks
Stefan Jaeger1, Alexandros Karargyris, Sameer Antani
1National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. stefan.jaeger@nih.gov
Summary
This study introduces an automated tuberculosis (TB) detection system for chest X-rays, offering an affordable solution for developing countries. The system achieved over 83% accuracy in identifying TB, aiding in early diagnosis and reducing mortality.
Area of Science:
- Medical Imaging
- Radiology
- Computer-Aided Diagnosis
Background:
- Tuberculosis (TB) poses a significant global health challenge, particularly in developing nations.
- Current diagnostic methods for TB are often slow, costly, and inaccessible in resource-limited settings.
- High mortality rates are associated with undiagnosed TB cases.
Purpose of the Study:
- To develop an automated, inexpensive system for detecting tuberculosis (TB) on chest radiographs.
- To enable large-scale TB screening in rural areas of developing countries with limited radiological resources.
- To improve the accuracy and efficiency of TB diagnosis.
Main Methods:
- Development of an automated TB detection system using conventional posteroanterior chest radiographs.
- Integration of a lung shape model, segmentation mask, and intensity model for enhanced lung segmentation.
- Validation of the system using data from a tuberculosis control program.
Main Results:
- The developed system achieved an area under the ROC curve exceeding 83%.
- Improved lung segmentation masks contributed to the system's performance.
- The system demonstrates potential for effective TB screening in resource-limited environments.
Conclusions:
- The automated TB detection system offers a promising, cost-effective solution for TB screening in developing countries.
- The approach enhances diagnostic capabilities where radiological expertise is scarce.
- This technology can significantly contribute to reducing the global burden of tuberculosis.
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