Lu Rui, Marziliano Pina, Hua Thng Choon
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:3296-9. doi: 10.1109/IEMBS.2005.1617181.
Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year. In order to make decisions such as liver resections, doctors will need to know the tumor volume, and further, the functional liver volume. Thus, an important task in radiology is the determination of tumor volume. Accurate segmentation of liver tumor from an abdominal image is one of the most important steps in 3D representation for liver volume measurement, liver transplant, and treatment planning[1]. Since manual segmenation is inconvenient, time consuming and depends on the individual operator to a large extent, automatic segmentation is much more preferred. In this paper, an active contour model is used to segment tumors from CT abdominal images. Initial boundary is manually placed by operators outside the tumor region. The snake deforms to the tumor boundary with the minimization of energy function. We then calculate the tumor volume using the series of segmented tumor slices. Results show that this method is quite efficient in tumor volume estimation compared with the WHO criteria, which measures the tumor by multiplying the longest perpendicular diameters.
肝癌是最常见的癌症疾病之一，每年导致大量死亡。为了做出诸如肝脏切除等决策，医生需要了解肿瘤体积，进而了解功能性肝体积。因此，放射学中的一项重要任务是确定肿瘤体积。从腹部图像中准确分割肝脏肿瘤是肝脏体积测量、肝移植和治疗规划的三维表示中最重要的步骤之一[1]。由于手动分割不方便、耗时且在很大程度上依赖于个体操作员，因此自动分割更受青睐。本文使用主动轮廓模型从腹部CT图像中分割肿瘤。初始边界由操作员手动放置在肿瘤区域之外。蛇形模型通过最小化能量函数变形至肿瘤边界。然后，我们使用一系列分割的肿瘤切片计算肿瘤体积。结果表明，与通过将最长垂直直径相乘来测量肿瘤的世界卫生组织标准相比，该方法在肿瘤体积估计方面相当有效。
