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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes
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Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes

机译：四腔心脏建模和3D心脏CT量自动分割

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Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.
机译：多腔室心脏分割是量化心脏功能的前提。在本文中，我们提出了一种自动心腔分割系统。开发这样的系统有两个密切相关的任务：心脏建模和自动模型拟合到看不见的体积。心脏是一个复杂的非刚性器官，具有四个腔室和几个主要的血管干。需要一个灵活而准确的模型来以适当的细节水平捕获心脏腔的形状。在我们的四腔表面网格模型中，要考虑和权衡以下两个因素：1）解剖结构的准确性和2）注释和自动检测的简便性。在我们的模型中明确表示了重要的标志，例如室间隔上的瓣膜和尖点。可以可靠地检测到这些界标，以指导自动模型拟合过程。我们还提出了两种机制，即基于旋转轴的重采样方法和基于平行切片的重采样方法，以建立网格点对应关系，这对于建立统计形状模型以在模型拟合过程中实施先验形状约束非常必要。使用此模型，我们为3D计算机断层扫描（CT）卷中的心腔自动分割开发了一种有效而强大的方法。我们的方法基于学习区分对象模型的最新进展，并且我们利用带注释的CT量的大型数据库。我们将分割公式化为两步学习问题：解剖结构定位和边界勾画。提出了一种新颖的算法，即边际空间学习（MSL），以解决9维相似度转换搜索问题，以定位心腔。确定心室的姿势后，我们通过基于学习的边界描绘来估计3D形状。大量实验证明了该方法的效率和鲁棒性，与最新技术相比具有优势。这是第一项报告，在具有323卷的大型心脏CT数据集上报告了稳定的结果。此外，我们对所有四个腔室进行自动分割的速度不到八秒。

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