School of Engineering Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada.
IEEE Trans Biomed Eng. 2011 Sep;58(9):2681-93. doi: 10.1109/TBME.2011.2161987. Epub 2011 Jul 14.
In this study, we propose a novel approach for accurate 3-D organ segmentation in the CT scan volumes. Instead of using the organ's prior information directly in the segmentation process, here we utilize the knowledge of the organ to validate a large number of potential segmentation outcomes that are generated by a generic segmentation process. For this, an organ space is generated based on the principal component analysis approach using which the fidelity of each segment to the organ is measured. We detail applications of the proposed method for the 3-D segmentation of human kidney and liver in computed tomography scan volumes. For evaluation, the public database of the MICCAI's 2007 grand challenge workshop has been incorporated. Implementation results show an average Dice similarity measure of 0.90 for the segmentation of the kidney. For the liver segmentation, the proposed algorithm achieves an average volume overlap error of 8.7 % and an average surface distance of 1.51 mm.
在这项研究中，我们提出了一种新的方法，用于在 CT 扫描体积中进行准确的 3D 器官分割。在这里，我们没有直接在分割过程中使用器官的先验信息，而是利用器官的知识来验证大量由通用分割过程生成的潜在分割结果。为此，我们使用主成分分析方法生成了一个器官空间，通过该空间可以衡量每个分割与器官的一致性程度。我们详细介绍了该方法在人体肾脏和肝脏 CT 扫描体积 3D 分割中的应用。为了评估，我们使用了 MICCAI 2007 年大型挑战赛研讨会的公共数据库。实现结果表明，肾脏分割的平均 Dice 相似性度量值为 0.90。对于肝脏分割，所提出的算法的平均体积重叠误差为 8.7%，平均表面距离为 1.51 毫米。
