For the problems of blurred edges, uneven background distribution, and many noise interferences in medical image segmentation, we proposed a medical image segmentation algorithm based on deep neural network technology, which adopts a similar U-Net backbone structure and includes two parts: encoding and decoding. Firstly, the images are passed through the encoder path with residual and convolutional structures for image feature information extraction. We added the attention mechanism module to the network jump connection to address the problems of redundant network channel dimensions and low spatial perception of complex lesions. Finally, the medical image segmentation results are obtained using the decoder path with residual and convolutional structures. To verify the validity of the model in this paper, we conducted the corresponding comparative experimental analysis, and the experimental results show that the DICE and IOU of the proposed model are 0.7826, 0.9683, 0.8904, 0.8069, and 0.9462, 0.9537 for DRIVE, ISIC2018 and COVID-19 CT datasets, respectively. The segmentation accuracy is effectively improved for medical images with complex shapes and adhesions between lesions and normal tissues.
Citation: Tongping Shen, Fangliang Huang, Xusong Zhang. CT medical image segmentation algorithm based on deep learning technology[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10954-10976. doi: 10.3934/mbe.2023485
For the problems of blurred edges, uneven background distribution, and many noise interferences in medical image segmentation, we proposed a medical image segmentation algorithm based on deep neural network technology, which adopts a similar U-Net backbone structure and includes two parts: encoding and decoding. Firstly, the images are passed through the encoder path with residual and convolutional structures for image feature information extraction. We added the attention mechanism module to the network jump connection to address the problems of redundant network channel dimensions and low spatial perception of complex lesions. Finally, the medical image segmentation results are obtained using the decoder path with residual and convolutional structures. To verify the validity of the model in this paper, we conducted the corresponding comparative experimental analysis, and the experimental results show that the DICE and IOU of the proposed model are 0.7826, 0.9683, 0.8904, 0.8069, and 0.9462, 0.9537 for DRIVE, ISIC2018 and COVID-19 CT datasets, respectively. The segmentation accuracy is effectively improved for medical images with complex shapes and adhesions between lesions and normal tissues.
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Tongping Shen, Fangliang Huang, Xusong Zhang. CT medical image segmentation algorithm based on deep learning technology[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10954-10976. doi: 10.3934/mbe.2023485
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