Segmentation and Visualization of a Large, High-Resolution Micro-CT Data of Mice

Abstract

High-resolution large datasets were acquired to improve the understanding of murine bone physiology. The purpose of this work is to present the challenges and solutions in segmenting and visualizing bone in such large datasets acquired using micro-CT scan of mice. The analyzed dataset is more than 50 GB in size with more than 6,000 2,048 × 2,048 slices. The study was performed to automatically measure the bone mineral density (BMD) of the entire skeleton. A global Renyi entropy (GREP) method was initially used for bone segmentation. This method consistently oversegmented skeletal region. A new method called adaptive local Renyi entropy (ALREP) is proposed to improve the segmentation results. To study the efficacy of the ALREP, manual segmentation was performed. Finally, a specialized high-end remote visualization system along with the software, VirtualGL, was used to perform remote rendering of this large dataset. It was determined that GREP overestimated the bone cross-section by around 30 % compared with ALREP. The manual segmentation process took 6,300 min for 6,300 slices while ALREP took only 150 min for segmentation. Automatic image processing with ALREP method may facilitate BMD measurement of the entire skeleton in a significantly reduced time, compared with manual process.