Segmentation of Ultrasound Images

描述（由申请人提供）：分割是在图像中找到边界的任务，是量化超声图像中信息的重要阶段。拟议的研究旨在建立一个计算框架分割人类和动物的心脏超声图像。它是基于两个想法：第一个使用的概率模型的超声信号和构成分割的MAP估计问题。第二个使用一种新的优化策略，称为隧道下降计算MAP估计。Tunnel下降有能力逃脱局部最大值的MAP对数似然函数。初步的结果，隧道下降的结果进行比较，手动分割，清楚地表明，隧道下降优于经典的活动轮廓分割短轴超声图像。实验评估也表明，它是强大的初始化和可靠的工作，而无需调整。本研究旨在将这些想法扩展到分割更复杂的边界，具有镜面反射的边界，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r.f.超声图像这些延伸段将用于在短轴和心尖四腔视图中联合分割心内膜和心外膜。将分割人类和实验室动物图像。这些图像将由共同研究者提供。将构建两个心脏超声体模，并用于评价分割的准确性以及基于分割的体积和增厚计算的准确性。R.F.还将收集来自体模的数据并在软件中系统地操作以创建B模式图像。r.f.将图像与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手动分割进行比较。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.