Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images
Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images
复制标题
DOI：
10.1007/978-3-642-02498-6_34
复制
发表时间：
2009-07
期刊：
影响因子：
--
通讯作者：
Yefeng Zheng;B. Georgescu;D. Comaniciu
中科院分区：
文献类型：
--
作者：
Yefeng Zheng;B. Georgescu;D. Comaniciu
最近，边缘空间学习（MSL）被提出作为用于在许多医学成像模态中自动检测3D解剖结构的通用方法[1]。为了准确定位3D对象，我们需要估计九个姿态参数（三个用于位置，三个用于方向，三个用于各向异性缩放）。MSL不再对原始的九维姿态参数空间进行穷举搜索，而是只对低维边缘空间进行搜索，提高了检测速度。在本文中，我们将MSL应用于2D对象检测，并对MSL和替代全空间学习（FSL）方法进行了全面的比较。对二维MRI图像左心室检测的实验表明，MSL在速度和精度上都优于FSL。此外，我们提出了两种新的技术，约束MSL和非刚性MSL，以进一步提高效率和精度。在许多真实的应用中，在相同边缘空间中的姿态参数之间可能存在强相关性。例如，大的对象可以沿沿着所有方向具有大的缩放值。受约束的MSL利用这种相关性来进一步加速。原始的MSL只估计图像中物体的刚性变换，因此不能准确定位大变形下的非刚性物体。提出的非刚性MSL直接估计非刚性变形参数，以提高定位精度。通过对226个腹部CT图像的肝脏检测对比实验，验证了所提方法的有效性。我们的系统只需不到一秒的时间就能准确检测到体积中的肝脏。
Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimen-sional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. Constrained MSL exploits this correlation for further speed-up. The original MSL only estimates the rigid transformation of an object in the image, therefore cannot accurately localize a nonrigid object under a large deformation. The proposed nonrigid MSL directly estimates the nonrigid deformation parameters to improve the localization accuracy. The comparison experiments on liver detection in 226 abdominal CT volumes demonstrate the effectiveness of the proposed methods. Our system takes less than a second to accurately detect the liver in a volume.