Abstract
Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.
Highlights
A basic task in medical image segmentation (IS) [1, 2] is to extract specific organs and tumors from different types of medical images
(3) e abovementioned improved 3D U-Net network is applied to the brain image dataset. e IS evaluation index is used to verify the experimental results, and the results show the effectiveness of the model used
In the Positive Predictive Value (PPV) index, compared with convolutional neural network (CNN), U-Net, and 3D U-net, the model used has increased by 10.50%, 5.95%, and 3.54%, respectively
Summary
A basic task in medical IS [1, 2] is to extract specific organs and tumors from different types of medical images. Ese small tumors are misdiagnosed as calcification or fatification or even ignored, leading to misdiagnosis or missed diagnosis of Scientific Programming cancer It is an arduous and complicated task to screen out images with tumors and segment them from a large number of medical images. Erefore, there is an urgent clinical need to develop a method that can accurately and automatically segment tumor regions from a large number of medical images to assist doctors in cancer diagnosis. Based on the encoding-decoding structure, U-Net [16] introduces skip connections to integrate low-level semantic information with high-level semantic information, which further improves the segmentation performance of the network. E above methods mainly use 2D models to segment clinical medical images. Erefore, this article uses 3D U-Net model to segment medical images. Based on the better segmentation performance of the used model, it has certain clinical significance for the diagnosis of diseases
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