Efficient Kidney Segmentation in Micro-CT Based on Multi-Atlas Registration and Random Forests

• Main Authors: Fengjun Zhao, Pei Gao, Haowen Hu, Xuelei He, Yuqing Hou, Xiaowei He
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Kidney segmentation; computed tomography; multi-atlas registration; random forests
• Online Access: https://ieeexplore.ieee.org/document/8423648/

Micro-computed tomography (micro-CT) provides an in vivo high-resolution preclinical imaging for murine kidneys. However, due to the relatively low dosage of X-rays, accurate and efficient segmentation of murine kidneys in micro-CT imaging remains challenging. In this paper, we proposed an efficient kidney segmentation method in micro-CT images based on multi-atlas registration (MAR) and random forests (RFs). First, we constructed a probability map of kidneys by the MAR and obtained an initial shape estimation of kidneys. We acquired the transformations of MAR based on high-contrast organs and then mapped kidneys to the new micro-CT images. Second, we extracted multiple features (including intensity, texture, and context features) from the voxels with lower probabilities and fed these features to a RF classifier. The role of RF is to fine-tune kidney boundaries after MAR. Finally, combining the initial shape with high probabilities and the fine-tuning of RF, we obtained the final segmentation of kidneys. The experiments were conducted on datasets acquired by micro-CT imaging of mice with and without the administration of contrast agent (Dataset1 and Dataset2). The results demonstrated the proposed MAR-RF outperformed the level sets, statistical-atlas registration, active shape model, and other supervised learning methods, with the Dice coefficients of 0.9766 and 0.9255, and the mean surface distances of 1.25 and 0.98 mm on Dataset1 and Dataset2, respectively. The training and prediction time of our MAR-RF were only 37.04% and 17.68% of the compared method, respectively. The proposed method has great potential for applications in other segmentation tasks of computer-aided diagnosis.