通过自身免疫性肝炎患儿肝脏超声波图像确定纤维化程度的系统

Ihor O. Ursu, Yulia S. Budnik, Oleksandr O. Shevchenko, Maryna B. Dyba, B. Tarasyuk, Volodymyr A. Pavlov
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引用次数: 0

摘要

简介弥漫性疾病是肝脏疾病中数量最多的一类。其中,自身免疫性肝炎因其在儿童中的严重病程而尤为突出。及时诊断和评估肝脏损伤程度是患者个性化治疗策略不可或缺的一部分。由于缺乏可靠的非侵入性肝病评估方法,影响了医疗服务的质量。因此,在超声波图像中寻找肝脏损伤的信息迹象以及改进解决多类分类问题的方法,是开发用于确定肝纤维化程度的无创系统的相关领域。目的通过多级分类系统改进肝纤维化分期诊断。方法。根据肝脏超声图像中血管的状态,提出并论证了一种基于神经网络的肝纤维化详细程度(八级)分类系统:第一级是通过卷积神经网络对感兴趣区的纤维化程度进行纤维化程度组分类,第二级是通过深度神经网络对感兴趣区的纤维化个体程度进行分类,第三级是整合第二级结果,得出患者(图像)整体的结论。为了优化特征空间,我们利用数据处理组法优化的逻辑多元回归模型进行了探索性分析。由此产生的一组广义变量构成了系统第二层的元特征空间。与使用单一卷积网络解决图像分类任务(输出八个类别)相比,该系统的分类质量提高了两倍。结果。提出了基于使用 ANN 解决多类问题的分层系统的改进版本。该系统利用描述肝脏血管状态的超声波图像,对自身免疫性肝炎患儿肝纤维化的详细程度进行分类。与使用基于卷积神经网络的标准多类分类器相比,使用分层分类系统使我们的分类准确率提高了 32.61%。分级系统的分类准确率:第一级--32.46%;第二级--50.43%;第三级--65.22%。结论文章提出、证实并开发了基于卷积神经网络的分级分类系统。与基于 СNNs 的标准多类分类器相比,使用该系统可将肝纤维化详细程度分类的准确性提高 2 倍。进一步提高该系统分类准确性的主要途径应该是将不同超声成像模式下获得的血管变形迹象和纹理特征结合起来。所开发的系统为改进基于图像分析的多类分类问题的解决方法提供了新的机遇。
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A system for Determining the Degree of Fibrosis by Ultrasound Images of the Liver of Children with Autoimmune Hepatitis
Introduction. Diffuse diseases are the most numerous class of liver diseases. Among them, autoimmune hepatitis stands out for its severe course in children. Its timely diagnosis and assessment of the degree of liver damage is an integral part of a patient’s personalised treatment strategy. The lack of reliable non-invasive methods for assessing liver disease affects the quality of medical services. Therefore, the search for informative signs of liver damage in ultrasound images and the improvement of methods for solving multi-class classification problems are relevant areas for the development of non-invasive systems for determining the degree of liver fibrosis. Purpose. Improve the diagnosis of liver fibrosis stages through a multi-level classification system. Methods. A system for classifying the detailed degree of fibrosis (eight classes) based on neural networks according to the state of the blood vessels in ultrasound images of the liver is proposed and substantiated: the first level is a fibrosis degrees group classification of fibrosis degree for regions of interest by convolutional neural networks, the second level is the classification of fibrosis individual degrees for regions of interest by a deep neural network, the third level is the integration of the second level results to obtain conclusions about the patient (image) as a whole. In order to optimize the feature space, we have performed an exploratory analysis using a logistic multivariate regression model optimized by the Group Method of Data Handling. The resulting set of generalized variables formed the meta-feature space for the second level of the system. A twofold increase in the quality of the system’s classification is shown in comparison with solving the task of image classification by a single convolutional network with an output of eight classes. Results. Improved version of the hierarchical system for solving multiclass problems based on the use of ANNs is proposed. The system implements the classification of the detailed degree of liver fibrosis in children with autoimmune hepatitis using ultrasound images characterizing the state of liver vessels. The use of a hierarchical classification system allowed us to obtain a classification accuracy of 32.61% higher than the use of a standard multi-class classifier based on a convolutional neural network. The classification accuracy of the hierarchical system: at the first level – 32.46%; at the second level – 50.43%; at the third level – 65.22%. Conclusion. The article proposes, substantiates and develops a hierarchical classification system based on convolutional neural networks. Its use makes it possible to increase the accuracy of classification of the detailed degree of liver fibrosis by 2 times compared to the standard multi-class classifier based on СNNs. The main source of further improvement of the classification accuracy of the system should be a combination of signs of vascular deformation and texture features that can be obtained with different ultrasound imaging modes. The developed system offers new opportunities for improving methods for solving multiclass classification problems based on image analysis.
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