Fully automated shape model positioning for bone segmentation in whole-body CT scans

Analysing osteolytic and osteoblastic bone lesions in systematically affected skeletons, e.g. in multiple myeloma or bone metastasis, is a complex task. Quantification of the degree of bone destruction needs segmentation of all lesions but cannot be managed manually. Automatic bone lesion detection is necessary. Our future objective is comparing modified bones with healthy shape models. For applying model based strategies successfully, identification and position information of single bones is necessary. A solution to these requirements based on bone medullary cavities is presented in this paper. Medullary cavities are useful for shape model positioning since they have similar position and orientation as the bone itself but can be separated more easily. Skeleton segmentation is done by simple thresholding. Inside the skeleton medullary cavities are segmented by a flood filling algorithm. The filled regions are considered as medullary cavity objects. To provide automatic shape model selection, medullary cavity objects are assigned to bone structures with pattern recognition. To get a good starting position for shape models, principal component analysis of medullary cavities is performed. Bone identification was tested on 14 whole-body low-dose CT scans of multiple myeloma patients. Random forest classification assigns medullary cavities of long bones to the corresponding bone (overall accuracy 90%). Centroid and first principal component of medullary cavity are sufficiently similar to those of bone (mean centroid difference 21.7 mm, mean difference angle 1.54◦ for all long bones of one example patient) and therefore suitable for shape model initialization. This method enables locating long bone structures in whole-body CT scans and provides useful information for a reasonable shape model initialization.