Dynamic cortex stripping for vertebra evaluation

James Stieger; Joseph E. Burns; Jianhua Yao; Ronald M. Summers

doi:10.1117/12.2082434

20 March 2015 Dynamic cortex stripping for vertebra evaluation

James Stieger, Joseph E. Burns, Jianhua Yao, Ronald M. Summers

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94143D (2015) https://doi.org/10.1117/12.2082434
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Vertebral cortex removal through cancellous bone reconstruction (CBR) algorithms on CT has been shown to enhance the detection rate of bone metastases by radiologists and reduce average reading time per case. Removal of the cortical bone provides an unobstructed view of the inside of vertebrae without any anomalous distractions. However, these algorithms rely on the assumption that the cortical bone of vertebrae can be removed without the identification of the endosteal cortical margin. We present a method for the identification of the endosteal cortical margin based on vertebral models and CT intensity information. First, triangular mesh models are created using the marching cubes algorithm. A search region is established along the normal of the surface and the image gradient is calculated at every point along the search region. The location with the greatest image gradient is selected as the corresponding point on the endosteal cortical margin. In order to analyze the strength of this method, ground truth and control models were also created. Our method was shown to have a significantly reduce the average error from 0.80 mm +/- 0.14 mm to 0.65 mm +/- 0.17 mm (p <0.0001) when compared to erosion. This method can potentially improve CBR algorithms, which improve visualization of cancellous bone lesions such as metastases, by more accurately identifying the inner wall of the vertebral cortex.

Citation Download Citation

James Stieger, Joseph E. Burns, Jianhua Yao, and Ronald M. Summers "Dynamic cortex stripping for vertebra evaluation", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94143D (20 March 2015); https://doi.org/10.1117/12.2082434

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