title: Multimodal μCT/μMR based semiautomated segmentation of rat vertebrae affected by mixed osteolytic/osteoblastic metastases.
abstract: OBJECTIVE
  Multimodal microimaging in preclinical models is used to examine the effect of spinal metastases on bony structure; however, the evaluation of tumor burden and its effect on microstructure has thus far been mainly qualitative or semiquantitative. Quantitative analysis of multimodality imaging is a time consuming task, motivating automated methods. As such, this study aimed to develop a low complexity semiautomated multimodal μCT/μMR based approach to segment rat vertebral structure affected by mixed osteolytic/osteoblastic destruction.
  
  
  METHODS
  Mixed vertebral metastases were developed via intracardiac injection of Ace-1 canine prostate cancer cells in three 4-week-old rnu/rnu rats. μCT imaging (for high resolution bone visualization), T1-weighted μMR imaging (for bone registration), and T2-weighted μMR imaging (for osteolytic tumor visualization) were conducted on one L1, three L2, and one L3 vertebrae (excised). One sample (L1-L3) was processed for undecalcified histology and stained with Goldner's trichome. The μCT and μMR images were registered using a 3D rigid registration algorithm with a mutual information metric. The vertebral microarchitecture was segmented from the μCT images using atlas-based demons deformable registration, levelset curvature evolution, and intensity-based thresholding techniques. The μCT based segmentation contours of the whole vertebrae were used to mask the T2-weighted μMR images, from which the osteolytic tumor tissue was segmented (intensity-based thresholding).
  
  
  RESULTS
  Accurate registration of μCT and μMRI modalities yielded precise segmentation of whole vertebrae, trabecular centrums, individual trabeculae, and osteolytic tumor tissue. While the algorithm identified the osteoblastic tumor attached to the vertebral pereosteal surfaces, it was limited in segmenting osteoblastic tissue located within the trabecular centrums.
  
  
  CONCLUSIONS
  This semiautomated segmentation method yielded accurate registration of μCT and μMRI modalities with application to the development of mathematical models analyzing the mechanical stability of metastatically involved vertebrae and in preclinical applications evaluating new and existing treatment effects on tumor burden and skeletal microstructure.
authors.affiliation: Orthopedic Biomechanics Laboratory, Sunnybrook Research Institute, Ontario M4N 3M5, Canada. parsa_hjjt@yahoo.ca
authors.forename: Seyed-Parsa
authors.initials: SP
authors.lastname: Hojjat
authors.forename: Warren
authors.initials: W
authors.lastname: Foltz
authors.forename: Lisa
authors.initials: L
authors.lastname: Wise-Milestone
authors.forename: Cari M
authors.initials: CM
authors.lastname: Whyne
mesh_terms: D000818:Animals; D001331:Automation; D045744:Cell Line, Tumor; D004285:Dogs; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009362:Neoplasm Metastasis; D010006:Osteoblasts / Q000000981:diagnostic imaging* / Q000473:pathology; D010014:Osteolysis; D011471:Prostatic Neoplasms / Q000473:pathology; D051381:Rats; D012680:Sensitivity and Specificity; D013125:Spinal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology / Q000556:secondary*; D055114:X-Ray Microtomography / Q000379:methods*