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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Interactive iterative relative fuzzy connectedness lung segmentation on thoracic 4D dynamic MR images
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Interactive iterative relative fuzzy connectedness lung segmentation on thoracic 4D dynamic MR images

机译：胸4D动态MR图像上的交互式迭代相对模糊连锁肺分割

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Lung delineation via dynamic 4D thoracic magnetic resonance imaging (MRI) is necessary for quantitative image analysis for studying pediatric respiratory diseases such as thoracic insufficiency syndrome (TIS). This task is very challenging because of the often-extreme malformations of the thorax in TIS, lack of signal from bone and connective tissues resulting in inadequate image quality, abnormal thoracic dynamics, and the inability of the patients to cooperate with the protocol needed to get good quality images. We propose an interactive fuzzy connectedness approach as a potential practical solution to this difficult problem. Manual segmentation is too labor intensive especially due to the 4D nature of the data and can lead to low repeatability of the segmentation results. Registration-based approaches are somewhat inefficient and may produce inaccurate results due to accumulated registration errors and inadequate boundary information. The proposed approach works in a manner resembling the Iterative Livewire tool but uses iterative relative fuzzy connectedness (IRFC) as the delineation engine. Seeds needed by IRFC are set manually and are propagated from slice-to-slice, decreasing the needed human labor, and then a fuzzy connectedness map is automatically calculated almost instantaneously. If the segmentation is acceptable, the user selects "next" slice. Otherwise, the seeds are refined and the process continues. Although human interaction is needed, an advantage of the method is the high level of efficient user-control on the process and non-necessity to refine the results. Dynamic MRI sequences from 5 pediatric TIS patients involving 39 3D spatial volumes are used to evaluate the proposed approach. The method is compared to two other IRFC strategies with a higher level of automation. The proposed method yields an overall true positive and false positive volume fraction of 0.91 and 0.03, respectively, and Hausdorff boundary distance of 2 mm.
机译：通过动态4D胸磁共振成像（MRI）的肺描绘是研究诸如胸虚综合征（TIS）的儿科呼吸疾病的定量图像分析所必需的。由于TIS中的胸腔的经常极端畸形，这项任务非常具有挑战性，从骨骼和结缔组织中缺乏信号，导致图像质量不足，异常胸动态，以及患者与所需的协议合作优质的形象。我们提出了一个互动模糊的连接方法，作为这种难题的潜在实际解决方案。手动分割过于劳动密集，特别是由于数据的4D性质，并且可以导致细分结果的可重复性。基于注册的方法有点低效，并且由于累积的登记误差和边界信息不足而可能产生不准确的结果。所提出的方法以类似于迭代LiveWire工具的方式工作，但使用迭代相对模糊连接（IRFC）作为DELINEATION发动机。 IRFC所需的种子是手动设定的，并从切片到切片传播，减少所需的人工劳动力，然后几乎瞬间自动计算模糊连接图。如果分割是可接受的，则用户选择“下一个”切片。否则，种子被精制，过程继续。虽然需要人类的相互作用，但该方法的优点是对过程的高效用户控制和不需要改进结果。来自涉及39个3D空间体积的5名儿科TIS患者的动态MRI序列用于评估所提出的方法。该方法与具有更高自动化水平的其他其他IRFC策略进行比较。所提出的方法分别产生0.91和0.03的总体真实阳性和假阳性体积分数，并且Hausdorff边界距离为2mm。

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