Automated segmentation of tumors on bone scans using anatomy-specific thresholding

Gregory H. Chu; Pechin Lo; Hyun J. Kim; Peiyun Lu; Bharath Ramakrishna; David Gjertson; Cheryce Poon; Martin Auerbach; Jonathan Goldin; Matthew S. Brown

doi:10.1117/12.911462

23 February 2012 Automated segmentation of tumors on bone scans using anatomy-specific thresholding

Gregory H. Chu, Pechin Lo, Hyun J. Kim, Peiyun Lu, Bharath Ramakrishna, David Gjertson, Cheryce Poon, Martin Auerbach, Jonathan Goldin, Matthew S. Brown

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83150F (2012) https://doi.org/10.1117/12.911462
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

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Gregory H. Chu, Pechin Lo, Hyun J. Kim, Peiyun Lu, Bharath Ramakrishna, David Gjertson, Cheryce Poon, Martin Auerbach, Jonathan Goldin, and Matthew S. Brown "Automated segmentation of tumors on bone scans using anatomy-specific thresholding", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150F (23 February 2012); https://doi.org/10.1117/12.911462

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