Segmentation of Ultrasound Images

描述（申请人提供）：超声图像分割是超声图像信息量化的重要阶段，是在图像中寻找边界的任务。提出的研究旨在建立一个计算框架分割人类和动物心脏超声图像。它基于两个思想：第一个是使用超声信号的概率模型，并将分割作为MAP估计问题。第二种方法使用一种新的优化策略，称为隧道下降来计算MAP估计。隧道下降具有逃避MAP对数似然函数局部极大值的能力。将隧道下降分割结果与人工分割结果进行比较的初步结果清楚地表明，隧道下降分割在短轴超声图像分割方面优于经典的主动轮廓。实验结果表明，该方法具有较强的初始化鲁棒性，无需调整即可可靠地工作。本研究试图将这些想法扩展到分割更复杂的边界，具有镜面的边界，具有数据丢失的边界，超声图像序列中的移动边界，3-D超声图像和射频超声图像。这些延伸将用于在短轴和根尖四室视图中共同分割心内膜和心外膜。人类和实验动物的图像将被分割。这些图像将由共同调查人员提供。建立两个心脏超声模型，用于评估分割的准确性以及基于分割的体积和增厚计算的准确性。来自幻影的射频数据也将被收集并在软件中系统地处理，以创建b模式图像。将rf图像的分割与b模式图像的分割进行比较，以了解机器处理对分割的影响。将人类和动物图像分割与人工分割进行比较。隧道下降的性能也将与模拟退火进行比较。

DESCRIPTION (provided by applicant): Segmentation, which is the task of finding boundaries in an image, is an important stage in quantifying information in ultrasound images. The proposed research seeks to build a computational framework for segmenting human and animal cardiac ultrasound images. It is based on two ideas: the first uses a probabilistic model for the ultrasound signal and poses segmentation as a MAP estimation problem. The second uses a new optimization strategy called tunneling descent to calculate the MAP estimate. Tunneling descent has the capacity to escape from local maxima of the MAP log-likelihood function. Preliminary results, which compare tunneling descent results to manual segmentation, clearly show that tunneling descent outperforms classical active contours in segmenting short-axis ultrasound images. Experimental evaluation also shows that it is robust with respect to initialization and works reliably without tweaking. This research seeks to extend these ideas to segment more complex boundaries, boundaries with specularities, boundaries with data dropout, moving boundaries in ultrasound image sequences, 3-D ultrasound images, and r.f. ultrasound images. These extensions will be used to jointly segment the endo and epi-cardium in short axis and apical four-chamber views. Human as well as laboratory animal images will be segmented. These images will be made available by the co-investigators. Two cardiac ultrasound phantoms will be built and used for evaluating the accuracy of segmentation as well as accuracy of volume and thickening calculations that are based on segmentation. R.F. data from the phantom will also be collected and systematically manipulated in software to create B-mode images. The segmentation of the r.f. images will be compared to the segmentation of the B-mode images to understand the effect of machine processing on segmentation. The human and animal image segmentations will be compared to manual segmentation. The performance of tunneling descent will also be compared to simulated annealing.