Segmentation of bone from ADC maps in pelvis area using local level-set and prior information

Lack of anatomical details in diffusion weighted magnetic resonance images limits their utilization and treatment response monitoring, shadowing the useful information they contain. Contemporary methods of utilizing these images are based on manual selection of region of interest, raising concerns about susceptibility of manual ROI placement to human errors, and limiting the investigation in specific spatial regions. In contrary to the whole body bone marrow segmentation with the luxury to include all the diseased bone marrow, high profile analysis could be applied. In this paper, we propose an automatic method for segmentation of pelvic bone with possible bone metastasis in apparent diffusion coefficient (ADC) maps. This method is a multi-parametric registration-segmentation method, taking advantage of prior information of the pelvic anatomy. Intensity inhomogeneity in the bone structure caused by bone marrow metastasis challenges the segmentation process on anatomical MR images. Specifically, we first build a probability map which provides shape and volume constraints for the segmentation. Then, T1-weighted MR images are rigidly registered to the probability map, and then the registered T1-weighted image is non-rigidly registered to its' corresponding ADC maps. Finally, the probability map is coupled with a local level set framework for automatic pelvic bone segmentation of the T1-weighted images. The segmented bone is used as a mask on the ADC map. The method is validated on 10 pairs of ADC/T1 images of breast cancer with bone marrow metastases patients. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.