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Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization

机译：3D CT量中的快速自动心脏隔离：最佳形状初始化

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Heart isolation (separating the heart from the proximity tissues, e.g., lung, liver, and rib cage) is a prerequisite to clearly visualize the coronary arteries in 3D. Such a 3D visualization provides an intuitive view to physicians to diagnose suspicious coronary segments. Heart isolation is also necessary in radiotherapy planning to mask out the heart for the treatment of lung or liver tumors. In this paper, we propose an efficient and robust method for heart isolation in computed tomography (CT) volumes. Marginal space learning (MSL) is used to efficiently estimate the position, orientation, and scale of the heart. An optimal mean shape (which optimally represents the whole shape population) is then aligned with detected pose, followed by boundary refinement using a learning-based boundary detector. Post-processing is further exploited to exclude the rib cage from the heart mask. A large-scale experiment on 589 volumes (including both contrasted and non-contrasted scans) from 288 patients demonstrates the robustness of the approach. It achieves a mean point-to-mesh error of 1.91 mm. Running at a speed of 1.5 s/volume, it is at least 10 times faster than the previous methods.

机译：心脏隔离（将心脏与邻近组织，例如肺，肝和肋骨保持架分离）是清晰显示3D冠状动脉的先决条件。这种3D可视化为医生提供了直观的视图，以诊断可疑的冠状动脉节段。在放射治疗计划中要屏蔽心脏以治疗肺部或肝脏肿瘤，也必须进行心脏隔离。在本文中，我们提出了一种在计算机断层扫描（CT）卷中进行心脏隔离的有效且鲁棒的方法。边际空间学习（MSL）用于有效地估计心脏的位置，方向和规模。然后将最佳平均形状（最佳表示整个形状总体）与检测到的姿势对齐，然后使用基于学习的边界检测器进行边界细化。后处理被进一步利用以从心脏面罩中排除肋骨。一项来自288位患者的589卷（包括对比扫描和非对比扫描）的大规模实验证明了该方法的鲁棒性。它实现的平均点对网格误差为1.91 mm。它以1.5秒/卷的速度运行，至少比以前的方法快10倍。

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《Machine learning in medical imaging》|2010年|p.84-91|共8页
会议地点 Beijing(CN);Beijing(CN);Beijing(CN);Beijing(CN)
作者
Yefeng Zheng; Fernando Vega-Higuera; Shaohua Kevin Zhou; Dorin Comaniciu;
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作者单位

Siemens Corporate Research, Princeton, USA;

Computed Tomography, Siemens Healthcare, Forchheim, Germany;

Siemens Corporate Research, Princeton, USA;

Siemens Corporate Research, Princeton, USA;

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正文语种 eng
中图分类医用物理学;
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机译：基于三级AdaBoost引导的主动形状模型的快速3D肝自动分割
2. Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model [J] . Gutiérrez-BeckerB., ArámbulaCosíoF., GuzmánHuertaM.E., Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering . 2013,第9期

机译：使用3D统计形状模型在超声体积上自动分割胎儿小脑
3. Automatic generation of 3D statistical shape models with optimal landmark distributions. [J] . Heimann T, Wolf I, Meinzer HP Methods of information in medicine . 2007,第3期

机译：自动生成具有最佳地标分布的3D统计形状模型。
4. Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization [C] . Yefeng Zheng, Fernando Vega-Higuera, Shaohua Kevin Zhou, International Workshop on Machine Learning in Medical Imaging . 2010

机译：3D CT卷中快速自动心脏隔离：最佳形状初始化
5. Fast algorithms for image segmentation and video target tracking with automatic initialization. [D] . Ma, Lei. 2005

机译：具有自动初始化功能的快速图像分割和视频目标跟踪算法。
6. 3D absolute shape measurement of live rabbit hearts with a superfast two-frequency phase-shifting technique [O] . Yajun Wang, Jacob I. Laughner, Igor R. Efimov, -1

机译：超快两频移相技术对活兔心脏进行3D绝对形状测量
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Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization

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