Abstract

The aim of this study was to explore the adoption value of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image intelligent segmentation model in the identification of nasopharyngeal carcinoma (NPC) lesions. The multisequence cross convolutional (MSCC) method was used in the complex convolutional network algorithm to establish the intelligent segmentation model two-dimensional (2D) ResUNet for the MRI image of the NPC lesion. Moreover, a multisequence multidimensional fusion segmentation model (MSCC-MDF) was further established. With 45 patients with NPC as the research objects, the Dice coefficient, Hausdorff distance (HD), and percentage of area difference (PAD) were calculated to evaluate the segmentation effect of MRI lesions. The results showed that the 2D-ResUNet model processed by MSCC had the largest Dice coefficient of 0.792 ± 0.045 for segmenting the tumor lesions of NPC, and it also had the smallest HD and PAD, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively. When batch size = 5, the convergence curve was relatively gentle, and the convergence speed was the best. The largest Dice coefficient of MSCC-MDF model segmenting NPC tumor lesions was 0.896 ± 0.09, and its HD and PAD were the smallest, which were 5.07 ± 0.54 mm and 14.41 ± 1.33%, respectively. Its Dice coefficient was lower than other algorithms (P < 0.05), but HD and PAD were significantly higher than other algorithms (P < 0.05). To sum up, the MSCC-MDF model significantly improved the segmentation performance of MRI lesions in NPC patients, which provided a reference for the diagnosis of NPC.

Highlights

  • Nasopharyngeal carcinoma (NPC) is a common malignant tumor, and the incidence of NPC in China accounts for 80% of the world [1]

  • After all magnetic resonance imaging (MRI) images were amplified by the training set and divided into image blocks, three sequence image blocks of T1WI, T2WI, and T1C were obtained. e data of the independent test set was input and saved, the Dice coefficients, Hausdorff distance (HD), and percentage of area difference (PAD) of the three sequence image blocks and the multisequence cross convolutional (MSCC) method to process the image blocks were compared, and the results were shown in Figure 3. e maximum Dice coefficient of MSCC was 0.792 ± 0.045, and its HD and PAD were the smallest, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively

  • Segmentation Results of Lymph Node Lesions in MRI Images Based on Convolutional neural network (CNN) Model. e segmentation of the lymph node lesions of NPC patients was analyzed (Figure 5), the Dice coefficient of MSCC was the largest (0.822 ± 0.077), and its HD and PAD were the smallest, which were 5.62 ± 0.62 mm and 16.92 ± 1.74%, respectively

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Summary

Introduction

Nasopharyngeal carcinoma (NPC) is a common malignant tumor, and the incidence of NPC in China accounts for 80% of the world [1]. X-ray, computed tomography (CT), positron emission computed tomography (PET), and magnetic resonance imaging (MRI) are often used to diagnose NPC patients. CT is currently the main method for locating NPC lesions, but CT diagnosis mainly relies on the tumor’s space-occupying effect on normal tissues, and it is difficult to find early small lesions without space-occupying effect on isodensity lesions and hidden parts [3]. E three sequences of T1W, T2W, and T1C in MRI images can clearly reflect the anatomical structure, tissue lesions, and microvessels, respectively. It is complementary in reflecting the structure of NPC tumors, lymph nodes, and surrounding tissues and organs and the occurrence of disease [5].

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