Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network

Min Zhao,Jun Yang,Jinling Zhang

doi:10.1155/2021/4614234

Abstract

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, P < 0.05 ) 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, P < 0.05 ). 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.

Highlights

Hepatocellular carcinoma (HCC) is a common malignancy disease with a high incidence in the global cancer statistics
When random data (RD) was not added, the DSC (0.724 ± 0.103) of the Gd-EOB-DTPA sequence segmentation was very close to the DSC (0.726 ± 0.079) of the double sequence segmentation. e addition of the portal sequence had almost no effect on the segmentation results based on the deep fusion network (DFN) algorithm
When RD was added, it increased the DSC of the Gd-EOB-DTPA single-sequence network by 1% and increased the DSC of the segmentation based on the DFN algorithm by 7.6%

Summary

Introduction

Hepatocellular carcinoma (HCC) is a common malignancy disease with a high incidence in the global cancer statistics. The global incidence rate has steadily increased to more than 672,000 people per year. Clinical studies showed that accurate segmentation of HCC lesions from high-quality magnetic resonance imaging (MRI) is a very important link in the treatment process [2, 3]. Traditional medical image segmentation segmented the lesions according to the shallow features of the images and relied on the clinical experience of the doctors. E addition of different subjective factors leads to different segmentation boundaries of lesions, resulting in large errors. The tedious and repetitive workload imposes a burden on medical staff [4, 5]

Methods

Results

Conclusion