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Proceedings Paper

Automated segmentation of tumors on bone scans using anatomy-specific thresholding
Author(s): Gregory H. Chu; Pechin Lo; Hyun J. Kim; Peiyun Lu; Bharath Ramakrishna; David Gjertson; Cheryce Poon; Martin Auerbach; Jonathan Goldin; Matthew S. Brown
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Paper 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.

Paper Details

Date Published: 23 February 2012
PDF: 8 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150F (23 February 2012); doi: 10.1117/12.911462
Show Author Affiliations
Gregory H. Chu, Univ. of California, Los Angeles (United States)
Pechin Lo, Univ. of California, Los Angeles (United States)
Hyun J. Kim, Univ. of California, Los Angeles (United States)
Peiyun Lu, Univ. of California, Los Angeles (United States)
Bharath Ramakrishna, Univ. of California, Los Angeles (United States)
David Gjertson, Univ. of California, Los Angeles (United States)
Cheryce Poon, Univ. of California, Los Angeles (United States)
Martin Auerbach, Univ. of California, Los Angeles (United States)
Jonathan Goldin, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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