CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron

Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of manual segmentation is low. In order to increase the generalization performance of segmentation model, we proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities. It was designed according to the multiscale input and encoding-decoding thoughts, and composed of multilayer perceptron and convolution modules. This model achieved communication of different channels and different spatial locations of each patch, and considers the edge related feature information between adjacent patches. Thus, it could fully extract global and local image information for the segmentation task. Meanwhile, this model met the effective segmentation of different structural lesion regions in different slices of three-dimensional medical images. In this experiment, the proposed CM-SegNet was trained, validated, and tested using six medical image datasets of different modalities and 5-fold cross validation method. The results showed that the CM-SegNet model had better segmentation performance and shorter training time for different medical images than the previous methods, suggesting it is faster and more accurate in automatic segmentation and has great potential application in clinic.

Automated summary

Accurate segmentation of lesions in medical images is crucial for clinical diagnosis and evaluation. This study proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities. The results showed that CM-SegNet had better segmentation performance and shorter training time than previous methods, indicating its potential for faster and more accurate automatic segmentation in clinical applications.