Hajiaghayi Mahdi, Groves Elliott M, Jafarkhani Hamid, Kheradvar Arash
IEEE Trans Biomed Eng. 2017 Jan;64(1):134-144. doi: 10.1109/TBME.2016.2542243. Epub 2016 Mar 31.
This study's objective is to develop and validate a fast automated 3-D segmentation method for cardiac magnetic resonance imaging (MRI). The segmentation algorithm automatically reconstructs cardiac MRI DICOM data into a 3-D model (i.e., direct volumetric segmentation), without relying on prior statistical knowledge.
A novel 3-D active contour method was employed to detect the left ventricular cavity in 33 subjects with heterogeneous heart diseases from the York University database. Papillary muscles were identified and added to the chamber using a convex hull of the left ventricle and interpolation. The myocardium was then segmented using a similar 3-D segmentation method according to anatomic information. A multistage approach was taken to determine the method's efficacy.
Our method demonstrated a significant improvement in segmentation performance when compared to manual segmentation and other automated methods.
A true 3-D reconstruction technique without the need for training datasets or any user-driven segmentation has been developed. In this method, a novel combination of internal and external energy terms for active contour was utilized that exploits histogram matching for improving the segmentation performance. This method takes advantage of full volumetric imaging, does not rely on prior statistical knowledge, and employs a convex-hull interpolation to include the papillary muscles.
本研究的目的是开发并验证一种用于心脏磁共振成像（MRI）的快速自动三维分割方法。该分割算法可自动将心脏MRI DICOM数据重建为三维模型（即直接容积分割），无需依赖先验统计知识。
采用一种新颖的三维活动轮廓方法，从约克大学数据库中检测33名患有不同心脏病的受试者的左心室腔。识别乳头肌，并使用左心室的凸包和插值法将其添加到心腔中。然后根据解剖信息，使用类似的三维分割方法对心肌进行分割。采用多阶段方法来确定该方法的有效性。
与手动分割和其他自动方法相比，我们的方法在分割性能上有显著提高。
已开发出一种无需训练数据集或任何用户驱动分割的真正三维重建技术。在该方法中，利用了活动轮廓的内部和外部能量项的新颖组合，通过直方图匹配来提高分割性能。该方法利用了全容积成像，不依赖先验统计知识，并采用凸包插值法纳入乳头肌。
