Kronman Achia, Joskowicz Leo
Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904 , Jerusalem, Israel.
Int J Comput Assist Radiol Surg. 2016 Mar;11(3):369-80. doi: 10.1007/s11548-015-1285-z. Epub 2015 Sep 4.
Patient-specific models of anatomical structures and pathologies generated from volumetric medical images play an increasingly central role in many aspects of patient care. A key task in generating these models is the segmentation of anatomical structures and pathologies of interest. Although numerous segmentation methods are available, they often produce erroneous delineations that require time-consuming modifications.
We present a new geometry-based algorithm for the reliable detection and correction of segmentation errors in volumetric medical images. The method is applicable to anatomical structures consisting of a few 3D star-shaped components. First, it detects segmentation errors by casting rays from the initial segmentation interior to its outer surface. It then classifies the segmentation surface into correct and erroneous regions by minimizing an energy functional that incorporates first- and second-order properties of the rays lengths. Finally, it corrects the segmentation errors by computing new locations for the erroneous surface points by Laplace deformation so that the new surface has maximum smoothness with respect to the rays-length gradient magnitude.
Our evaluation on initial segmentations of 16 abdominal aortic aneurysm and 12 lung tumors in CT scans obtained by both adaptive region-growing and active contours level-set segmentation improved the volumetric overlap error by 66 and 70.5% respectively, with respect to the ground-truth.
The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.
从容积医学图像生成的特定患者的解剖结构和病变模型在患者护理的许多方面发挥着越来越核心的作用。生成这些模型的一项关键任务是对感兴趣的解剖结构和病变进行分割。尽管有许多分割方法可用，但它们常常产生错误的轮廓，需要耗时的修改。
我们提出了一种基于几何的新算法，用于可靠地检测和校正容积医学图像中的分割错误。该方法适用于由几个三维星形组件组成的解剖结构。首先，通过从初始分割内部向其外表面投射光线来检测分割错误。然后，通过最小化一个包含光线长度一阶和二阶属性的能量泛函，将分割表面分类为正确和错误区域。最后，通过拉普拉斯变形计算错误表面点的新位置来校正分割错误，以使新表面相对于光线长度梯度幅度具有最大平滑度。
我们对通过自适应区域生长和活动轮廓水平集分割获得的CT扫描中的16个腹主动脉瘤和12个肺肿瘤的初始分割进行评估，相对于真实情况，体积重叠误差分别提高了66%和70.5%。
我们方法的优点是它独立于覆盖各种解剖结构和病变的初始分割算法，不需要形状先验，并且需要最少的用户交互。
