A novel approach to segment and classify regional lymph nodes on computed tomography images.

Morphology of lymph nodal metastasis is critical for diagnosis and prognosis of cancer patients. However, accurate prediction of lymph node type based on morphological information is rarely available due to lack of pathological validation. To obtain correct morphological information, lymph nodes must be segmented from computed tomography (CT) image accurately. In this paper we described a novel approach to segment and predict the status of lymph nodes from CT images and confirmed the diagnostic performance by clinical pathological results. We firstly removed noise and preserved edge details using a revised nonlinear diffusion equation, and secondly we used a repulsive-force-based snake method to segment the lymph nodes. Morphological measurements for the characterization of the node status were obtained from the segmented node image. These measurements were further selected to derive a highly representative set of node status, called feature vector. Finally, classical classification scheme based on support vector machine model was employed to simulate the prediction of nodal status. Experiments on real clinical rectal cancer data showed that the prediction performance with the proposed framework is highly consistent with pathological results. Therefore, this novel algorithm is promising for status prediction of lymph nodes.

淋巴结转移的形态对于癌症患者的诊断和预后至关重要。然而，由于缺乏病理验证，基于形态信息对淋巴结类型进行准确预测的情况很少见。为了获得正确的形态信息，必须从计算机断层扫描（CT）图像中准确地分割出淋巴结。在本文中，我们描述了一种从CT图像中分割和预测淋巴结状态的新方法，并通过临床病理结果证实了其诊断性能。我们首先使用修正的非线性扩散方程去除噪声并保留边缘细节，其次使用基于斥力的蛇形方法来分割淋巴结。从分割后的淋巴结图像中获取用于表征淋巴结状态的形态学测量值。进一步选择这些测量值以得出一组具有高度代表性的淋巴结状态，称为特征向量。最后，采用基于支持向量机模型的经典分类方案来模拟淋巴结状态的预测。对真实临床直肠癌数据的实验表明，所提出的框架的预测性能与病理结果高度一致。因此，这种新算法在淋巴结状态预测方面很有前景。