Esophageal tissue segmentation on OCT images with hybrid attention network

The accurate segmentation of the tissue layers of Optical Coherence Tomography (OCT) esophageal images has vital guiding significance for esophageal diseases research and computer-aided diagnosis. Existing automatic segmentation algorithms based on fully convolutional networks due to the lack of context information resulting in inaccurate segmentation performance. In this paper, we propose a hybrid attention network (HAN) to address this problem. The proposed framework includes an encoder attention module, a channel attention module and a tailored pyramid pooling module (TPPM). The encoder attention module can make up for the loss of image details due to down-sampling operation. The channel attention module is designed to selectively emphasize interdependent channel by integrating associated features among all channel maps. The tailored pyramid pooling module is designed to enhance the spatial contextual information. The combination of channel and spatial contextual information helps to boost feature discriminability, the framework has high segmentation accuracy. Through experiments, it can be verified that the network outperforms several advanced deep learning frameworks in segmentation. The clinical diagnostic potential of HAN for eosinophilic esophagitis (EOE), an esophageal disease, is also demonstrated in the experiment.