MFA U-Net: a U-Net like multi-stage feature analysis network for medical image segmentation

The U-Net and its extensions have achieved good success in medical image segmentation. However, fine-grained segmentation of the objects at their fuzzy edges, which is commonly found in medical images, is still challenging. In this paper, we propose a U-Net like Multi-Stage Feature Analysis Network (MFA U-Net) for medical image segmentation, which focus on mining the reusability of the images and features from several perspectives. Firstly, a multi-channel dimensional feature extraction module is proposed, where the input image was reused by multiple branches of convolutions with different channels to generate supplement features to the original U shaped network. Next, a cascaded U-shaped network is designed for deeper feature mining and analysis, which enables progressive refinement of the features. In the neck of the cascaded network, a parallel hybrid convolution module is designed that concatenating several types of convolutional methods to enhance the semantic representation ability of the model. In short, by reusing of the input images and detected features in several stages, more effective features were extracted and the segmentation performances were improved. The proposed algorithm was evaluated by three mainstream 2D color medical image segmentation datasets and gets significant improvements compared with the traditional U-Net framework, as well as the latest improved ones. Compared to the baseline network, it gets the improvements of 0.93% (Dice) and 1.45% (IoU) on GlaS, 2.09% (Dice) and 2.87% (IoU) on MoNuSeg, and 0.17% (F1) and 1.72% (SE) on DRIVE.