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

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.

[1]  O Munck,et al.  What do early bone scans tell about breast cancer patients? , 1982, European journal of cancer & clinical oncology.

[2]  Mattias Ohlsson,et al.  A new computer-based decision-support system for the interpretation of bone scans , 2006, Nuclear medicine communications.

[3]  G. Andriole,et al.  The natural history, skeletal complications, and management of bone metastases in patients with prostate carcinoma , 2000, Cancer.

[4]  N. Ayache,et al.  Fast Non Rigid Matching by Gradient Descent: Study and Improvements of the "Demons" Algorithm , 1999 .

[5]  Lars Edenbrandt,et al.  Quality of planar whole-body bone scan interpretations—a nationwide survey , 2008, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[7]  Susan Halabi,et al.  Design and end points of clinical trials for patients with progressive prostate cancer and castrate levels of testosterone: recommendations of the Prostate Cancer Clinical Trials Working Group. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Peter J Ell,et al.  Clinical audit in nuclear medicine. , 2004, Nuclear medicine communications.

[9]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[10]  S M Larson,et al.  Quantitative bone metastases analysis based on image segmentation. , 1997, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[11]  Seok Ki Kim,et al.  Comparison of Image Enhancement Methods for the Effective Diagnosis in Successive Whole-Body Bone Scans , 2011, Journal of Digital Imaging.