Abstract：
OBJECTIVE: Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algorithms, which in turn allow the prediction of possible complications and enable risk assessment, which is crucial in saving lives. The aim of this paper is to present a novel fully automatic 3D segmentation method, which combines basic image processing techniques and more advanced machine learning algorithms, for detecting and modelling the aorta in 3D CT imaging data.
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.
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.
摘要：
目的：主动脉分割在临床实践中非常有用，允许诊断多种病理，比如解剖，动脉瘤和闭塞性疾病。在这种情况下,图像分割是应用诊断算法的先决条件，这反过来可以预测可能的并发症并进行风险评估，这对拯救生命至关重要。本文的目的是提出一种新颖的全自动三维分割方法，它结合了基本的图像处理技术和更先进的机器学习算法，用于在3DCT成像数据中检测和建模主动脉。
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。
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。
