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首页> 外文会议>International Conference on Advanced Technologies for Signal and Image Processing >Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging
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Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

机译：使用宫颈癌MR成像进行半自动淋巴结分割和分类

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The segmentation and classification of Lymph Nodes (LNs) is a fundamental but challenging step in the analysis of medical images of cervical cancer. Both tasks can leverage morphological features such as size, shape, contour, and heterogeneous appearance. However, these features might vary with the progressive state of LNs. Hence, accurate detection of LNs boundary is an essential step sing to classify LN as suspect (malignant) and non-suspect (benign). However, manual delineation of LNs might produce classification errors due to the inter and intra-observer variability. Semi-automatic and automatic LNs segmentation methods are greatly desired as they would help improve patient diagnosis and treatment processes. Currently, Magnetic Resonance Imaging (MRI) is widely used to diagnose cervical cancer and LN involvement. Diffusion Weighted (DW)-MRI exhibits metastatic LN as bright regions. This paper presents a semi-automatic segmentation and classification method of LNs. Specifically, we propose a novel approach which leverages (1) the complementarity of structural and diffusion MR images through a fusion step and (2) morphological features of the segmented metastatic LNs for classification. The contribution of our proposed algorithm is threefold. First, we fuse the axial T2-Weighted (T2-w) anatomical image and the DW image. Second, we detect LNs using region-growing method in order to compute the final classification. Third, segmentation results are then used to classify LNs based on a gray level dependency matrix technique which extracts LN features. We evaluated our method using 10 MR images T2-w and DW with 47 metastatic LNs. We obtained an average accuracy of 70.21% for cervical cancer nodule classification.
机译：淋巴结（LNs）的分割和分类是宫颈癌医学图像分析中的一个基本但具有挑战性的步骤。两项任务都可以利用形态特征，例如大小，形状，轮廓和异构外观。但是，这些功能可能会随着LN的渐进状态而变化。因此，准确检测LN边界是将LN分为可疑（恶性）和非可疑（良性）的必不可少的步骤。但是，由于观察者之间和观察者内部的可变性，手动描绘LN可能会产生分类错误。迫切需要半自动和自动LN分割方法，因为它们将有助于改善患者的诊断和治疗过程。当前，磁共振成像（MRI）被广泛用于诊断宫颈癌和LN累及。扩散加权（DW）-MRI表现为转移性LN为亮区。本文提出了一种LNs的半自动分割与分类方法。具体而言，我们提出了一种新颖的方法，该方法利用（1）通过融合步骤的结构和扩散MR图像的互补性，以及（2）分段转移LN的形态特征进行分类。我们提出的算法的贡献是三方面的。首先，我们融合轴向T2加权（T2-w）解剖图像和DW图像。其次，我们使用区域增长方法检测LN，以计算最终分类。第三，然后基于灰度依赖矩阵技术，使用分割结果对LN进行分类，该技术提取了LN特征。我们使用10个MR图像T2-w和DW以及47个转移性LN评估了我们的方法。对于宫颈癌结节分类，我们获得了70.21％的平均准确度。

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