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首页> 外文会议>4th International Symposium on Bioelectronics and Bioinformatics >Segmentation of liver metastasis on CT images using the marker-controlled watershed and fuzzy connectedness algorithms
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Segmentation of liver metastasis on CT images using the marker-controlled watershed and fuzzy connectedness algorithms

机译：标记控制的分水岭和模糊连接算法在CT图像上肝转移的分割

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Accurate quantification for the size of liver metastasis on CT images is critical to surgery/treatment planning and therapy response assessment. To date, there are still no practical methods for automatic or semiautomatic segmentation of liver metastases. This paper presents a method, which combines the marker-controlled watershed transform and fuzzy connectedness algorithm for semiautomatic delineation of liver metastases on contrast-enhanced sequential CT images. The key to successful use of marker-controlled watershed transform is to reliably determine internal and external markers, which is also the focus of this work. With the fuzzy connectedness technique, we propose a practical method to determine the internal and external markers for the liver metastasis in CT images. The performance of the proposed method was evaluated over 30 liver metastases from 10 patients. The results manually delineated by a radiologist served as the ???gold standard??? for comparison. The preliminary results have shown the potential of this algorithm for the segmentation of liver metastases on CT images.
机译：在CT图像上准确量化肝转移的大小对于手术/治疗计划和治疗反应评估至关重要。迄今为止，尚无用于肝转移的自动或半自动分割的实用方法。本文提出了一种方法，将标记物控制的分水岭变换和模糊连接算法相结合，用于在对比增强的连续CT图像上半自动描绘肝转移。成功使用标记控制的分水岭变换的关键是可靠地确定内部和外部标记，这也是这项工作的重点。利用模糊连接技术，我们提出了一种实用的方法来确定CT图像中肝转移的内部和外部标记。在10例患者的30例肝转移中评估了该方法的性能。由放射科医生手动描述的结果用作“金标准”。为了比较。初步结果表明该算法可用于CT图像上肝转移的分割。

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