Mr. Robert D. Ambrosini Profile

Robert D. Ambrosini

Graduate Student at Univ of Rochester Medical Ctr

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Proceedings Article | 9 March 2010 Paper

Realistic simulated lung nodule dataset for testing CAD detection and sizing

Robert Ambrosini, Walter O'Dell

Proceedings Volume 7624, 76242X (2010) https://doi.org/10.1117/12.843996

KEYWORDS: Lung, Computer simulations, Computed tomography, Computer aided diagnosis and therapy, Databases, Spherical lenses, Computer aided design, Tumors, 3D image reconstruction, Algorithm development

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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.

Proceedings Article | 28 February 2009 Paper

Volume change determination of metastatic lung tumors in CT images using 3D template matching

Robert Ambrosini, Peng Wang, Walter O'Dell

Proceedings Volume 7260, 726035 (2009) https://doi.org/10.1117/12.813593

KEYWORDS: Lung, Spherical lenses, Computed tomography, Tumors, 3D modeling, 3D image processing, Computer simulations, Reconstruction algorithms, Computer aided diagnosis and therapy, Gold

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The ability of a clinician to properly detect changes in the size of lung nodules over time is a vital element to both the diagnosis of malignant growths and the monitoring of the response of cancerous lesions to therapy. We have developed a novel metastasis sizing algorithm based on 3-D template matching with spherical tumor appearance models that were created to match the expected geometry of the tumors of interest while accounting for potential spatial offsets of nodules in the slice thickness direction. The spherical template that best-fits the overall volume of each lung metastasis was determined through the optimization of the 3-D normalized cross-correlation coefficients (NCCC) calculated between the templates and the nodules. A total of 17 different lung metastases were extracted manually from real patient CT datasets and reconstructed in 3-D using spherical harmonics equations to generate simulated nodules for testing our algorithm. Each metastasis 3-D shape was then subjected to 10%, 25%, 50%, 75% and 90% scaling of its volume to allow for 5 possible volume change combinations relative to the original size per each reconstructed nodule and inserted back into CT datasets with appropriate blurring and noise addition. When plotted against the true volume change, the nodule volume changes calculated by our algorithm for these 85 data points exhibited a high degree of accuracy (slope = 0.9817, R² = 0.9957). Our results demonstrate that the 3-D template matching method can be an effective, fast, and accurate tool for automated sizing of metastatic tumors.

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