Hardisty M, Gordon L, Agarwal P, Skrinskas T, Whyne C
Orthopaedic Biomechanics Laboratory, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Room UB-19, Toronto, Ontario, M4N 3M5, Canada.
Med Phys. 2007 Aug;34(8):3127-34. doi: 10.1118/1.2746498.
Quantitative assessment of metastatic disease in bone is often considered immeasurable and, as such, patients with skeletal metastases are often excluded from clinical trials. In order to effectively quantify the impact of metastatic tumor involvement in the spine, accurate segmentation of the vertebra is required. Manual segmentation can be accurate but involves extensive and time-consuming user interaction. Potential solutions to automating segmentation of metastatically involved vertebrae are demons deformable image registration and level set methods. The purpose of this study was to develop a semiautomated method to accurately segment tumor-bearing vertebrae using the aforementioned techniques. By maintaining morphology of an atlas, the demons-level set composite algorithm was able to accurately differentiate between trans-cortical tumors and surrounding soft tissue of identical intensity. The algorithm successfully segmented both the vertebral body and trabecular centrum of tumor-involved and healthy vertebrae. This work validates our approach as equivalent in accuracy to an experienced user.
骨转移疾病的定量评估通常被认为是无法测量的，因此，有骨转移的患者常常被排除在临床试验之外。为了有效量化转移性肿瘤累及脊柱的影响，需要对椎体进行准确分割。手动分割虽然准确，但需要大量且耗时的用户交互。用于自动分割转移累及椎体的潜在解决方案是恶魔可变形图像配准和水平集方法。本研究的目的是使用上述技术开发一种半自动化方法来准确分割有肿瘤的椎体。通过保持图谱的形态，恶魔-水平集复合算法能够准确区分穿皮质肿瘤和相同强度的周围软组织。该算法成功分割了受累肿瘤椎体和健康椎体的椎体及小梁中心。这项工作验证了我们的方法在准确性上与有经验的用户相当。
