ABSTRACT

Kidney tumors are the second most frequent urology tumors. They are of many types, mostly existing as malignant tumors. In order to improve the accuracy of segmentation and classification of kidney tumors, this paper proposes to build a model of simultaneous segmentation and classification of kidney tumors based on convolutional neural networks to assist medical experts in diagnosis. A two-task neural network 2D SCNet is proposed by combining kidney tumor segmentation and classification. Based on our proposed framework, classification can feed back the global contextual information of the network, and segmentation can make the network focus on local features and regions of interest (ROI). Both tasks jointly promote network feature learning and both increase each other’s prior information. The combination of segmentation and classification of 2D SCNet can achieve an accuracy rate of 99.5% in both benign and malignant classification. The results of the ‘2D SCNet + three-label’ segmentation reached Dice coefficients of 0.946 and 0.846, respectively. Compared with PSPNet, our network kidney and tumor segmentation results are improved by 4.9% and 5.0%, respectively, which shows that the addition of classification module is beneficial to the learning of segmentation network. From the cross-validation results, we can see that 2D SCNet and the two-step segmentation strategy can obtain better results in the segmentation and classification tasks of kidney tumors. The base network of 2D SCNet can extract networks for any feature. This paper compares Res Net50+ PPM and Dense Net as the results of segmentation and classification of the base network. Res Net50+ PPM obtains better results. 2D SCNet can help to segment and examine kidney tumors more efficiently and accurately.

摘要

肾脏肿瘤是第二常见的泌尿外科肿瘤。它们的种类很多，多以恶性肿瘤的形式存在。为提高肾脏肿瘤分割分类的准确性，本文提出建立基于卷积神经网络的肾脏肿瘤同时分割分类模型，辅助医学专家进行诊断。通过结合肾脏肿瘤分割和分类，提出了一种双任务神经网络 2D SCNet。基于我们提出的框架，分类可以反馈网络的全局上下文信息，而分割可以使网络专注于局部特征和感兴趣区域（ROI）。这两个任务共同促进了网络特征学习，并且都增加了彼此的先验信息。2D SCNet 的分割和分类相结合，无论是良性还是恶性的分类准确率都可以达到 99.5%。'2D SCNet + 三标签'分割的结果分别达到了 0.946 和 0.846 的 Dice 系数。与PSPNet相比，我们的网络肾脏和肿瘤分割结果分别提高了4.9%和5.0%，说明分类模块的加入有利于分割网络的学习。从交叉验证结果可以看出，2D SCNet和两步分割策略在肾脏肿瘤的分割和分类任务中可以获得更好的结果。2D SCNet 的基础网络可以为任何特征提取网络。本文比较了Res Net50+ PPM和Dense Net作为基础网络的分割分类结果。Res Net50+ PPM 获得更好的结果。2D SCNet 可以帮助更有效、更准确地分割和检查肾脏肿瘤。