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Prior Knowledge for Targeted Object Segmentation in Medical Images

机译:医学图像中目标对象分割的先验知识

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摘要

Medical image segmentation, the task of partitioning an image into meaningful parts, is an important step toward automating medical image analysis and is at the crux of a variety of medical imaging applications, such as computer aided diagnosis, therapy planning and delivery, and computer aided interventions. However, existence of noise, low contrast and objectsu27 complexity in medical images preclude ideal segmentation. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results on targeted objects segmentation. In this thesis, we develop novel techniques to augment optimization-based segmentation frameworks with different types of prior knowledge to identify and delineate only those objects (targeted objects) that conform to specific geometrical, topological and appearance priors. These techniques include employing prior knowledge to segment multi-part objects with part-configuration constraints and encoding priors based on images acquired from different imaging equipment and of differing dimensions. Our objective is to satisfy two important aspects in optimization-based image segmentation: (1) fidelity-optimizability trade-off, and (2) space and time complexity.Particularly, in our first contribution, we adopt several prior information to build a faithful objective function unconcerned about its convexity to segment potentially overlapping cells with complex topology. In our second contribution, we improve the space and time complexity and augment the level sets framework with the ability to handle geometric constraints between boundaries of multi-region objects. In our first two contributions we opt for ensuring the objective function is flexible enough (even if it is non-convex) to accurately capture the intricacies of the segmentation problem. In our third contribution, we focus on optimizability. We propose a convex formulation to augment the popular Mumford-Shah model and develop a new regularization term to incorporate similar geometrical and distance prior as our second contribution while maintaining global optimality. Lastly, we efficiently incorporate different types of priors based on images acquired from different imaging equipment (different modalities) and of dissimilar dimensions to segment multiple objects in intraoperative multi-view endoscopic videos. We show how our technique allows for the inclusion of laparoscopic camera motion model to stabilize the segmentation.
机译:医学图像分割是将图像划分为有意义的部分的任务,是朝着自动化医学图像分析迈出的重要一步,并且正处于各种医学成像应用程序(例如计算机辅助诊断,治疗计划和交付以及计算机辅助)的症结中干预。然而,医学图像中存在的噪声,低对比度和物体复杂性妨碍了理想的分割。事实证明,将先验知识整合到图像分割算法中对于获得针对目标对象分割的更准确和合理的结果很有用。在本文中,我们开发了新颖的技术来增强具有不同类型的先验知识的基于优化的分割框架,以仅识别和描绘那些符合特定几何,拓扑和外观先验的对象(目标对象)。这些技术包括利用先验知识对具有零件配置约束的多零件对象进行分割,并基于从不同成像设备和不同尺寸获取的图像对先验编码。我们的目标是满足基于优化的图像分割的两个重要方面:(1)保真度-优化性的权衡,以及(2)空间和时间的复杂性。特别是在我们的第一篇论文中,我们采用了一些先验信息来构建忠实的目标函数不关心其凸度,以分割具有复杂拓扑结构的可能重叠的单元格。在我们的第二个贡献中,我们改善了空间和时间的复杂性,并增强了具有处理多区域对象边界之间的几何约束的能力的水平集框架。在我们的前两个贡献中,我们选择确保目标函数足够灵活(即使它是非凸的)也可以准确地捕获细分问题的复杂性。在我们的第三项贡献中,我们关注可优化性。我们提出了一个凸公式,以扩充流行的Mumford-Shah模型,并开发一个新的正则化项,以将相似的几何形状和距离合并为我们的第二个贡献,同时保持全局最优性。最后,我们基于从不同的成像设备(不同的模态)获得的图像和不同尺寸的图像有效地合并了不同类型的先验,以分割术中多视图内窥镜视频中的多个对象。我们展示了我们的技术如何允许纳入腹腔镜摄像机运动模型来稳定分割。

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    Nosrati Seyed Masoud;

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  • 年度 2015
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