Metastatic Vertebrae Segmentation for Use in a Clinical Pipeline

Abstract

Vertebral metastases are common complications of primary cancers that alter bone architecture potentially leading to vertebral fracture and neurological compromise. Quantitative measures from vertebral body segmentations from Computed Tomography (CT) scans have been useful for assessing fracture risk predictions and vertebrae stability. Previous segmentation methods used to generate these metrics were slow and required manual intervention, limiting their utility. More accurate, robust and fast methods are needed for clinical assessments. This investigation proposes a 3D U-Net Convolutional Neural Network (CNN) to accurately segment individual trabecular centrum from metastatically compromised vertebrae of interest in CT imaging. Using different augmentation techniques achieved good performance (DSC = 0.904 ± 0.056) with the segmentation model remaining accurate with simulated lower image quality, and translation of the vertebrae within the image, especially compared to when no augmentations were used (DSC = 0.774 ± 0.188). Integration of this method into a clinical tool will allow accurate and robust quantitative assessment of mechanical stability, aiding clinical decision making to improve patient care.