Preliminary results of automated removal of degenerative joint disease in bone scan lesion segmentation

Gregory H. Chu; Pechin Lo; Hyun J. Kim; Martin Auerbach; Jonathan Goldin; Keith Henkel; Ashley Banola; Darren Morris; Heidi Coy; Matthew S. Brown

doi:10.1117/12.2008082

18 March 2013 Preliminary results of automated removal of degenerative joint disease in bone scan lesion segmentation

Gregory H. Chu, Pechin Lo, Hyun J. Kim, Martin Auerbach, Jonathan Goldin, Keith Henkel, Ashley Banola, Darren Morris, Heidi Coy, Matthew S. Brown

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867007 (2013) https://doi.org/10.1117/12.2008082
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Whole-body bone scintigraphy (or bone scan) is a highly sensitive method for visualizing bone metastases and is the accepted standard imaging modality for detection of metastases and assessment of treatment outcomes. The development of a quantitative biomarker using computer-aided detection on bone scans for treatment response assessment may have a significant impact on the evaluation of novel oncologic drugs directed at bone metastases. One of the challenges to lesion segmentation on bone scans is the non-specificity of the radiotracer, manifesting as high activity related to non-malignant processes like degenerative joint disease, sinuses, kidneys, thyroid and bladder. In this paper, we developed an automated bone scan lesion segmentation method that implements intensity normalization, a two-threshold model, and automated detection and removal of areas consistent with non-malignant processes from the segmentation. The two-threshold model serves to account for outlier bone scans with elevated and diffuse intensity distributions. Parameters to remove degenerative joint disease were trained using a multi-start Nelder-Mead simplex optimization scheme. The segmentation reference standard was constructed manually by a panel of physicians. We compared the performance of the proposed method against a previously published method. The results of a two-fold cross validation show that the overlap ratio improved in 67.0% of scans, with an average improvement of 5.1% points.

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Gregory H. Chu, Pechin Lo, Hyun J. Kim, Martin Auerbach, Jonathan Goldin, Keith Henkel, Ashley Banola, Darren Morris, Heidi Coy, and Matthew S. Brown "Preliminary results of automated removal of degenerative joint disease in bone scan lesion segmentation", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867007 (18 March 2013); https://doi.org/10.1117/12.2008082

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