Cellular Automata Segmentation of the Boundary between the Compacta of Vertebral Bodies and Surrounding Structures
This work addresses the need for faster and more accurate spinal disease diagnosis in aging populations, though it is incremental as it applies an existing algorithm to a specific medical imaging task.
The paper tackled the problem of reducing time and error in vertebral boundary segmentation for spinal imaging by applying the GrowCut algorithm, achieving a Dice Similarity Coefficient of 82.99% and reducing segmentation time to under six minutes compared to manual methods.
Due to the aging population, spinal diseases get more and more common nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States. Thus the numbers of surgical spinal procedures are also increasing with the aging population and precise diagnosis plays a vital role in reducing complication and recurrence of symptoms. Spinal imaging of vertebral column is a tedious process subjected to interpretation errors. In this contribution, we aim to reduce time and error for vertebral interpretation by applying and studying the GrowCut-algorithm for boundary segmentation between vertebral body compacta and surrounding structures. GrowCut is a competitive region growing algorithm using cellular automata. For our study, vertebral T2-weighted Magnetic Resonance Imaging (MRI) scans were first manually outlined by neurosurgeons. Then, the vertebral bodies were segmented in the medical images by a GrowCut-trained physician using the semi-automated GrowCut-algorithm. Afterwards, results of both segmentation processes were compared using the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD) which yielded to a DSC of 82.99+/-5.03% and a HD of 18.91+/-7.2 voxel, respectively. In addition, the times have been measured during the manual and the GrowCut segmentations, showing that a GrowCut-segmentation - with an average time of less than six minutes (5.77+/-0.73) - is significantly shorter than a pure manual outlining.