METHODS: An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach, based on a Markov Random Field network. The result of the proposed two-stage segmentation process is modelled and visualized.
RESULTS: The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D models. The performance of segmentation was evaluated both qualitatively and quantitatively against other commonly used segmentation techniques, in terms of the accuracy achieved, compared to the actual aorta, which was defined manually by experts.
CONCLUSIONS: The proposed methodology achieved superior segmentation performance, compared to all compared segmentation techniques, in terms of the accuracy of the extracted 3D aortic model. Therefore, the proposed segmentation scheme could be used in clinical practice, such as in treatment planning and assessment, as it can speed up the evaluation of the medical imaging data, which is commonly a lengthy and tedious process.
方法:基于初始强度阈值的分割过程之后是基于分类的分割方法,基于马尔可夫随机场网络。对所提出的两阶段分割过程的结果进行建模和可视化。
结果:将提出的方法应用于16个3DCT数据集,并将提取的主动脉段重建为3D模型。对其他常用的分割技术进行了定性和定量的分割性能评估,就达到的准确性而言,与实际的主动脉相比,由专家手动定义。
结论:所提出的方法实现了优越的分割性能,与所有比较的分割技术相比,在提取的3D主动脉模型的准确性方面。因此,所提出的分割方案可用于临床实践,例如在治疗计划和评估中,因为它可以加快医学成像数据的评估,这通常是一个漫长而乏味的过程。