MH-Net: Model-data-driven hybrid-fusion network for medical image segmentation

Highlights：

• We propose a loss function that combines contour length and region information and adds an elastic energy term to it, which tackles the problem of the unsmooth borders caused by purely network training.

• We introduce a curvature based regularization function into the segmentation model to regularize the contour length term and improve the over-fitting phenomenon in network training.

• We use a new network of a dual-path feature extraction module (DP-FEM) with channel attention and a high-level and low-level feature fusion module (HLFFM) with spatial attention, which improves the accuracy of training.

• The results of the proposed fusion network segmentation are analysed with multiple existing models. The results prove that our proposed model can obtain reasonable image segmentation results on datasets, such as remote projection and fundus colour images.