Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

Zhou Hongzhen; Wang Shuyuan; Zhang Tao; Liu Demei; Yang Kevin

摘要

The purpose of this study was to explore the value of extraction of tumor features in contrast-enhanced ultrasonography (CEUS) images based on the deep belief networks (DBN) for the diagnosis of cervical cancer patients and realize the intelligent evaluation on effects of diagnosis and chemotherapy of the cervical cancer. An automatic extraction algorithm with the time-intensity curve (TIC) was proposed based on Sparse nonnegative matrix factorization (SNMF) in this study, and was applied to the framework of automatic analysis of cervical cancer tumors based on the deep belief networks, to assist doctors in the analysis of cervical cancer tumors. The framework was applied to the real clinical diagnostic data, and the feasibility of the method was verified by comparing the accuracy, sensitivity, and specificity. Later, the parameters of patients' time to peak (TP), peak intensity (PI), mean transit time (MTT), and area under the curve (AUC) were obtained by drawing TICs, and the changes of p53 protein and ki-67 protein obtained by pathological section staining were analyzed to evaluate the therapeutic effect in the patients. It was found that the proposed model of tumor feature extraction based on the DBN had the higher accuracy (86.36%), sensitivity (83.33%), and specificity (87.50%). The related parameters of TIC curve obtained based on SNMF showed that there was a significant difference in p53 content between tissues with different degrees of disease (p 0.05), the PI of poorly differentiated tissues was significantly higher than that of those with high to medium differentiation (p 0.05). In addition, PI and AUC of patients after chemotherapy were significantly lower than that before chemotherapy (p 0.05), while MTT was significantly higher than that before chemotherapy (p 0.05). Therefore, the proposed TIC feature extraction of CEUS images based on SNMF and the automatic tumor classification based on deep learning can be used in the diagnosis and efficacy evaluation of cervical cancer patients.

机译：本研究的目的是探讨基于深度信仰网络（DBN）对宫颈癌患者的深度信仰网络（DBN）进行对比增强的超声（CEUS）图像中肿瘤特征的价值，并实现诊断效应的智能评估宫颈癌的化疗。基于本研究中的稀疏非负基质分子（SNMF）提出了一种具有时间强度曲线（TIC）的自动提取算法，并应用于基于深度信仰网络的宫颈癌肿瘤自动分析框架，协助医生在分析宫颈癌肿瘤。该框架应用于真实的临床诊断数据，通过比较精度，灵敏度和特异性来验证该方法的可行性。后来，通过拉伸TIC获得患者峰值（TP），峰强度（PI），峰值强度（PI），平均转动时间（MTT）和区域下的面积，以及P53蛋白和ki-的变化通过病理截面染色获得的67蛋白分析以评估患者的治疗效果。结果发现，基于DBN的肿瘤特征萃取模型具有更高的精度（86.36％），灵敏度（83.33％）和特异性（87.50％）。基于SNMF获得的TIC曲线的相关参数显示，在不同程度的疾病之间组织之间的P53含量有显着差异（P <0.05），差异化组织的PI显着高于高于中等的组织分化（P <0.05）。此外，化疗后患者的PI和AUC显着低于化疗前（P <0.05），而MTT显着高于化疗前的疗法（P <0.05）。因此，基于SNMF和基于深度学习的自动肿瘤分类的CEUS图像提出的TIC特征提取可用于宫颈癌患者的诊断和功效评估。

Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

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