基于CNN与Transformer的医学图像分割

医学图像对疾病的诊断、治疗和评估均有所帮助,准确分割医学图像中的器官对于辅助医生的诊断具有重要的实际意义.由于医学图像中各器官部位与周围组织的图像对比度低,不同器官的边缘和形状也会存在很大差异,从而增加了分割的难度.针对这些问题,本文提出了一种基于卷积神经网络和Transformer的医学图像语义分割网络,有效提高了医学图像语义分割的精度.特征提取部分使用ResNet-50网络结构,在特征提取后使用Transformer模块来扩大感受野.在上采样过程中加入多个跳跃连接层,充分利用各阶段的特征提取信息,来恢复至与输入图像相近的分辨率.在胃肠道医学图像分割数据集上的实验结果证明本文的方法可以有效分割医学图像中的器官组织,提升分割准确率.

Medical Image Segmentation Based on CNN and Transformer

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Accurate segmentation of organs in medical images is of great practical significance to assist doctors in diagnosis. Due to the low contrast between organ parts and surrounding tissues in medical images, the edges and shapes of different organs are very different, which increases the difficulty of segmentation. To solve these problems, this study proposes a semantic segmentation network for medical images based on a convolutional neural network and Transformer, which effectively improves the accuracy of semantic segmentation of medical images. The feature extraction part uses a ResNet-50 network structure, and a Transformer module is employed to expand the receptive field after feature extraction. In the process of up-sampling, multiple skip connection layers are added, and the feature extraction information of each stage is fully utilized to make the resolution close to that of input images. The experimental results on the segmentation dataset of gastrointestinal medical images prove that the proposed method can effectively segment organs and tissues in medical images and improve the segmentation accuracy.