Realistic simulated lung nodule dataset for testing CAD detection and sizing

Robert D. Ambrosini; Walter G. O'Dell

doi:10.1117/12.843996

9 March 2010 Realistic simulated lung nodule dataset for testing CAD detection and sizing

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76242X (2010) https://doi.org/10.1117/12.843996
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

The development of computer-aided diagnosis (CAD) methods for the processing of CT lung scans continues to become increasingly popular due to the potential of these algorithms to reduce image reading time, errors caused by user fatigue, and user subjectivity when screening for the presence of malignant lesions. This study seeks to address the critical need for a realistic simulated lung nodule CT image dataset based on real tumor morphologies that can be used for the quantitative evaluation and comparison of these CAD algorithms. The manual contouring of 17 different lung metastases was performed and reconstruction of the full 3-D surface of each tumor was achieved through the utilization of an analytical equation comprised of a spherical harmonics series. 2-D nodule slice representations were then computed based on these analytical equations to produce realistic simulated nodules that can be inserted into CT datasets with well-circumscribed, vascularized, or juxtapleural borders and also be scaled to represent nodule growth. The 3-D shape and intensity profile of each simulated nodule created from the spherical harmonics reconstruction was compared to the real patient CT lung metastasis from which its contour points were derived through the calculation of a 3-D correlation coefficient, producing an average value of 0.8897 (±0.0609). This database of realistic simulated nodules can fulfill the need for a reproducible and reliable gold standard for CAD algorithms with regards to nodule detection and sizing, especially given its virtually unlimited capacity for expansion to other nodule shape variants, organ systems, and imaging modalities.

Citation Download Citation

Robert D. Ambrosini and Walter G. O'Dell "Realistic simulated lung nodule dataset for testing CAD detection and sizing", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242X (9 March 2010); https://doi.org/10.1117/12.843996

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