GFNet: Automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features

Highlights：

• We design a Edge-guidance map that contains the boundary features of each layer to further extract the boundary information when the features of each layer are extracted. The experiment proves that this design is very effective.

• We applied the GFNet framework to VGG16 and used our method on two different datasets. One data set was “seen” to verify learning ability, and the other was “not seen” to verify generalization ability. Experimental results show that GFNet has better learning ability and generalization ability than existing models.

• We conducted experiments with each model on training datasets of different sizes. Our model can achieve good performance when the training set is relatively small. In real world case decision making, our GFNet is fully capable of such tasks if it is put into application under time constraints or with few training samples. Our GFNet can also be sufficiently trained to achieve maximum performance if there is sufficient time or a large training sample.

摘要

•A novel deep learning network framework, GFNet, was proposed for the segmentation of infected region of COVID-19 in two-dimensional CT images of lungs. By aggregating the high-layer features using the aggregation module, the aggregated features can capture context information and generate a global location map as an initial boot region for subsequent steps. In order to further dig the boundary information of the target, we use the reverse attention module step by step from the high-layer to the low-layer, then further extract the hidden details of each layer, and finally fuse the features of each layer, so that the network can fully extract the details that are difficult to be noticed by the previous model.•We design a Edge-guidance map that contains the boundary features of each layer to further extract the boundary information when the features of each layer are extracted. The experiment proves that this design is very effective.•We applied the GFNet framework to VGG16 and used our method on two different datasets. One data set was “seen” to verify learning ability, and the other was “not seen” to verify generalization ability. Experimental results show that GFNet has better learning ability and generalization ability than existing models.•We conducted experiments with each model on training datasets of different sizes. Our model can achieve good performance when the training set is relatively small. In real world case decision making, our GFNet is fully capable of such tasks if it is put into application under time constraints or with few training samples. Our GFNet can also be sufficiently trained to achieve maximum performance if there is sufficient time or a large training sample.