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Multimodal μCT/μMR based semiautomated segmentation of rat vertebrae affected by mixed osteolytic/osteoblastic metastases
Author(s) -
Hojjat SeyedParsa,
Foltz Warren,
WiseMilestone Lisa,
Whyne Cari M.
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3703590
Subject(s) - thresholding , medicine , segmentation , image registration , medical imaging , nuclear medicine , radiology , anatomy , artificial intelligence , computer science , image (mathematics)
Purpose: 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.