Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier

Jianhua Yao; Joseph E. Burns; Vic Sanoria; Ronald M. Summers

doi:10.1117/1.JMI.4.2.024504

5 June 2017 Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier

Jianhua Yao, Joseph E. Burns, Vic Sanoria, Ronald M. Summers

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 2, 024504 (June 2017). https://doi.org/10.1117/1.JMI.4.2.024504

Abstract

Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient’s treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection. A combination of watershed, graph cut, and level set algorithms is first run to obtain the initial detections. Detections are then sent to multiple classifiers for sclerotic, lytic, and mixed lesions. The system was tested on 44 cases with 225 sclerotic, 139 lytic, and 92 mixed lesions. The results showed that sensitivity (false positive per patient) was 0.81 (2.1), 0.81 (1.3), and 0.76 (2.1) for sclerotic, lytic, and mixed lesions, respectively. It also demonstrates that using PET/CT data significantly improves the computer aided detection performance over using CT alone.

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Jianhua Yao, Joseph E. Burns, Vic Sanoria, and Ronald M. Summers "Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier," Journal of Medical Imaging 4(2), 024504 (5 June 2017). https://doi.org/10.1117/1.JMI.4.2.024504

Received: 16 November 2016; Accepted: 16 May 2017; Published: 5 June 2017

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