Park JinHyeong, Zhou S Kevin, Jackson John, Comaniciu Dorin
Integrated Data Systems, Siemens Corporate Research, Inc., Princeton, NJ, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):983-90. doi: 10.1007/978-3-540-85988-8_117.
Doppler echocardiography is widely used for functional assessment of heart valves such as mitral valve. In current clinical work flow, to extract Doppler measurements, the envelopes of acquired Doppler spectra are manually traced. We propose a robust algorithm for automatically tracing the envelopes of mitral valve inflow Doppler spectra, which exhibit a large amount of variations in envelope shape and image appearance due to various disease conditions, patient/sonographer/instrument differences, etc. The algorithm is learning-based and capable of fully automatic detection and segmentation of the mitral inflow structures. Experiments show that the algorithm, running within one second, yields comparable performance to experts.
多普勒超声心动图广泛用于心脏瓣膜（如二尖瓣）的功能评估。在当前的临床工作流程中，为了提取多普勒测量值，需要手动追踪获取的多普勒频谱包络。我们提出了一种鲁棒算法，用于自动追踪二尖瓣流入多普勒频谱的包络，由于各种疾病状况、患者/超声检查人员/仪器差异等原因，这些频谱的包络形状和图像外观存在大量变化。该算法基于学习，能够对二尖瓣流入结构进行全自动检测和分割。实验表明，该算法在一秒内运行，性能与专家相当。
