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Updated: Jul 15, 2026
A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Automated detection of small pulmonary nodules in whole lung CT scans
Andinet A Enquobahrie1, Anthony P Reeves, David F Yankelevitz
1School of Electrical and Computer Engineering, Rhodes Hall, Cornell University, Ithaca, NY 14850, USA. andy@ece.cornell.edu
Rationale And Objectives:
The objective of this work was to develop and evaluate a robust algorithm that automatically detects small solid pulmonary nodules in whole lung helical CT scans from a lung cancer screening study.
Materials And Methods:
We developed a three-stage detection algorithm for both isolated and attached nodules. The algorithm consisted of nodule search space demarcation, nodule candidates' generation, and a sequential elimination of false positives. Isolated nodules are nodules that are surrounded by lung parenchyma, whereas attached nodules are connected to large, dense structures such as pleural and/or mediastinal surface. Two large well-documented whole lung CT scan databases (Databases A and B) were created to train and test the detection algorithm. Database A contains 250 sequentially selected scans with 2.5-mm slice thickness that were obtained at Weill Medical College of Cornell University. With equipment upgrade at this college, a second database, Database B, was created containing 250 scans with a 1.25-mm slice thickness. A total of 395 and 482 nodules were identified in Databases A and B, respectively. In both databases, the majority of the nodules were isolated, comprising 72.1% and 82.3% of nodules in Databases A and B, respectively.
Results:
The detection algorithm was trained and tested on both Databases A and B. For isolated nodules with sizes 4 mm or larger, the algorithm achieved 94.0% sensitivity and 7.1 false positives per case (FPPC) for Database A (2.5 mm). Similarly, the algorithm achieved 91% sensitivity and 6.9 FPPC for Database B (1.25 mm). The algorithm achieved 92% sensitivity with 17.4 FPPC and 89% sensitivity with 5.5 FFPC for attached nodules with sizes 3 mm or larger in the Database A (2.5 mm) and Database B (1.25 mm), respectively.
Conclusion:
The developed algorithm achieved practical performance for automated detection of both isolated and the more challenging attached nodules. The automated system will be a useful tool to assist radiologists in identifying nodules from whole lung CT scans in a clinical setting.
