Automatic Bone Segmentation in Pelvis Area with Bone Marrow Metastases; Applications in Breast Cancer Treatment Monitoring

Abstract

Background/Objectives: In bone marrow metastatic cancers, therapy goals are delay skeletal-related events, reduce symptoms, improve patients’ life quality and increase their survival. Correct knowledge about patients’ bone state in shortest possible time is the key to achieve therapy goals. Therefore, several quantitative analyses of MR images have been used, in which accurate segmentation of the bone plays a vital role. Due to heterogeneous nature of tumors and presence of cystic or necrotic areas, correct separation of the overall border of bone is difficult, creating source of error for the quantitative analysis results. Diffusion-weighted (DW) MR images are capable of revealing information of bone status, showing applications in skeletal related diseases. The poor anatomy available in these images gears them toward anatomical MR images such as T1-wighted (T1-w) images. However, boundary distortion created by bone metastasis hierarchically affects information extracted from DW-MR images. Commonly used bone segmentation methods are applicable on healthy bone structures, while they partly fail in presence of bone metastasis, especially in advanced situations. In this study, we proposed an automatic method to extract pelvis bone from T1-w images in the presence of bone marrow metastasis. This method eliminates manual segmentation variability, resulting reproducible quantitative analysis independent of human error. We also investigated clinical quantitative analyses variations caused by miss-segmentation of the bone due to the bone marrow metastasis, and how our proposed method eliminates such errors.