Nowadays, the proficiency of Computer-Aided Diagnosis systems for early diagnosis of malignant nodules in baseline Computed Tomography (CT) scan of chest crucially depends on the authenticity of the segmented nodule. In this study, the authors introduce a new morphological feature called solidity radius (SR). They then employ this feature in the new segmentation framework for the automatic segmentation of nodule and the attached vessels around the seed point on the nodule, delineated by an expert. In the framework, they extract the SR and the curvature features and employ them to determine the candidate pixels of the nodule. They then use the convex-hull image of the candidate pixels to surround the nodule area. Afterward, using the region growing on the Hessian-based vesselness enhancement map, the attached vessels are labelled. Finally, they apply the traditional solidity feature of the segmented nodule and the pattern of the related skeleton to prune the false positive pieces. They validate the introduced approach on two datasets, including 56 and 481 CTs (containing 1205 nodules). They show the proficiency of their SR-based approach compared to the state-of-the-art methods with average Dice Similarity Coefficients of 77.98 and 77.47% for the two datasets, respectively.

中文翻译：

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使用结节的形态特征和拓扑骨架在胸部CT扫描中对肺结节和附着血管进行分割

如今，计算机辅助诊断系统在早期胸部胸部CT扫描中对恶性结节进行早期诊断的能力关键取决于分段结节的真实性。在这项研究中，作者介绍了一种新的形态特征，称为实心半径（SR）。然后，他们在新的分割框架中采用此功能，以自动分割结节和结节种子点附近的附着血管，并由专家进行描述。在框架中，他们提取SR和曲率特征，并使用它们来确定结节的候选像素。然后，他们使用候选像素的凸包图像围绕结节区域。之后，使用基于Hessian的血管增强地图上生长的区域，对附着的血管进行标记。最后，他们利用分段结节的传统坚固性特征和相关骨骼的样式来修剪假阳性碎片。他们在包括56个和481个CT（包含1205个结节）的两个数据集上验证了引入的方法。与最先进的方法相比，他们展示了他们基于SR的方法的熟练程度，两个数据集的平均骰子相似系数分别为77.98和77.47％。