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Automatic Sclerotic Bone Metastases Detection in the Pelvic Region From Dual Energy CT

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D Fehr

D Fehr*, C Schmidtlein , S Hwang , J Deasy , H Veeraraghavan , Memorial Sloan Kettering Cancer Center, New York, NY


TU-G-204-2 (Tuesday, July 14, 2015) 4:30 PM - 6:00 PM Room: 204

Purpose:To automatically detect sclerotic bone metastases in the pelvic region using dual energy computed tomography (DECT).

Methods:We developed a two stage algorithm to automatically detect sclerotic bone metastases in the pelvis from DECT for patients with multiple bone metastatic lesions and with hip implants. The first stage consists of extracting the bone and marrow regions by using a support vector machine (SVM) classifier. We employed a novel representation of the DECT images using multi-material decomposition, which represents each voxel as a mixture of different physical materials (e.g. bone+water+fat). Following the extraction of bone and marrow, in the second stage, a bi-histogram equalization method was employed to enhance the contrast to reveal the bone metastases. Next, meanshift segmentation was performed to separate the voxels by their intensity levels. Finally, shape-based filtering was performed to extract the possible locations of the metastatic lesions using multiple shape criteria. We used the following shape parameters: area, eccentricity, major and minor axis, perimeter and skeleton.

Results:A radiologist with several years of experience with DECT manually labeled 64 regions consisting of metastatic lesions from 10 different patients. However, the patients had many more metastasic lesions throughout the pelvis. Our method correctly identified 46 of the marked 64 regions (72%). In addition, our method also identified several other lesions, which can then be validated by the radiologist. The missed lesions were typically very large elongated regions consisting of several islands of very small (<4mm) lesions.

Conclusion:We developed an algorithm to automatically detect sclerotic lesions in the pelvic region from DECT. Preliminary assessment shows that our algorithm generated lesions agreeing with the radiologist generated candidate regions. Furthermore, our method reveals additional lesions that can be inspected by the radiologist, thereby, reducing radiologist effort in identifying all the lesions with poor contrast from the DECT images.

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