CMLCNet: medical image segmentation network based on convolution capsule encoder and multi-scale local co-occurrence

Medical images have low contrast and blurred boundaries between different tissues or between tissues and lesions. Because labeling medical images is laborious and requires expert knowledge, the labeled data are expensive or simply unavailable. UNet has achieved great success in the field of medical image segmentation. However, the pooling layer in downsampling tends to discard important information such as location information. It is difficult to learn global and long-range semantic interactive information well due to the locality of convolution operation. The usual solution is increasing the number of datasets or enhancing the training data though augmentation methods. However, to obtain a large number of medical datasets is tough, and the augmentation methods may increase the training burden. In this work, we propose a 2D medical image segmentation network with a convolutional capsule encoder and a multiscale local co-occurrence module. To extract more local detail and contextual information, the capsule encoder is introduced to learn the information about the target location and the relationship between the part and the whole. Multi-scale features can be fused by a new attention mechanism, which can then selectively emphasize salient features useful for a specific task by capturing global information and suppress background noise. The proposed attention mechanism is used to preserve the information that is discarded by pooling layers of the network. In addition, a multi-scale local co-occurrence algorithm is proposed, where the context and dependencies between different regions in an image can be better learned. Experimental results on the dataset of Liver, ISIC and BraTS2019 show that our network is superior to the UNet and other previous medical image segmentation networks under the same experimental conditions.