To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In addition, RD experiment and single-input experiment were also designed. It was found that the sensitivity (0.595 ± 0.145) and DSC (0.587 ± 0.113) obtained by improved U-Net were significantly higher than the sensitivity (0.405 ± 0.098) and DSC (0.468 ± 0.115, ) obtained by U-Net. The sensitivity of multisequence MRI image segmentation algorithm based on DFN (0.779 ± 0.015) was significantly higher than that of FCN algorithm (0.604 ± 0.056, ). The multisequence MRI image segmentation algorithm based on the DFN had higher indicators for liver cancer lesions than those of the improved U-Net. When RD was added, it not only increased the DSC of the single-sequence network enhanced by the hepatocyte-specific magnetic resonance contrast agent (Gd-EOB-DTPA) by 1% but also increased the DSC of the multisequence MRI image segmentation algorithm based on DFN by 7.6%. In short, the improved U-Net can significantly improve the recognition rate of small lesions in liver cancer patients. The addition of RD strategy improved the segmentation indicators of liver cancer lesions of the DFN and can fuse image features of multiple sequences, thereby improving the accuracy of lesion segmentation.

中文翻译：

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深度卷积神经网络下肝癌患者磁共振图像自动分割算法

为了研究不同序列的磁共振成像 (MRI) 图像对肝细胞癌 (HCC) 病灶分割的影响，改进了 U-Net。此外，引入深度融合网络（DFN）、数据增强策略和随机数据（RD）策略，提出了一种基于DFN的多序列MRI图像分割算法。设计了单序列MRI图像和多序列MRI图像的分割实验，并将单序列MRI图像的分割结果与卷积神经网络（FCN）算法的分割结果进行了比较。此外，还设计了RD实验和单输入实验。发现改进的U-Net得到的灵敏度（0.595±0.145）和DSC（0.587±0.113）明显高于灵敏度（0.405±0.098）和DSC（0.468±0.115，)由 U-Net 获得。基于DFN的多序列MRI图像分割算法的灵敏度（0.779±0.015）显着高于FCN算​​法（0.604±0.056，）。基于DFN的多序列MRI图像分割算法对肝癌病变的指标高于改进的U-Net。添加RD后，不仅使肝细胞特异性磁共振造影剂（Gd-EOB-DTPA）增强的单序列网络的DSC增加了1%，而且使基于多序列MRI图像分割算法的DSC增加了1%。 DFN 增长 7.6%。总之，改进后的U-Net可以显着提高肝癌患者小病灶的识别率。RD策略的加入提高了DFN对肝癌病灶的分割指标，可以融合多个序列的图像特征，从而提高病灶分割的准确性。