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Proceedings Paper

Preliminary results of automated removal of degenerative joint disease in bone scan lesion segmentation
Author(s): Gregory H. Chu; Pechin Lo; Hyun J. Kim; Martin Auerbach; Jonathan Goldin; Keith Henkel; Ashley Banola; Darren Morris; Heidi Coy; Matthew S. Brown
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Paper 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.

Paper Details

Date Published: 18 March 2013
PDF: 10 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867007 (18 March 2013); doi: 10.1117/12.2008082
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)
Martin Auerbach, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Jonathan Goldin, Univ. of California, Los Angeles (United States)
David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Keith Henkel, MedQIA (United States)
Ashley Banola, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Darren Morris, MedQIA (United States)
Heidi Coy, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)
David Geffen School of Medicine, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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