Abstract
The effective segmentation of esophagus and esophageal cancer from Computed Tomography (CT) images can meaningfully assist doctors in the diagnosis and treatment of esophageal cancer patients. However, problems such as the small proportion of the esophageal region in CT images and the irregular shape of the esophagus will make the diagnosis difficult. In practical applications, not all esophagus and esophageal cancer morphology can be included in the training set, so the generalization ability of the model is very important. These are the difficulties in segmenting the esophagus and esophageal cancer. Since some adjacent tissues and organs of the esophagus are visually close to the esophagus and esophageal cancer, how to ensure that the network can extract effective distinguishing features has become the focus of research. In this paper, a novel U-Net structure - Channel-attention U-Net is proposed to segment esophagus and esophageal cancer from CT slices. This novel network combines a Channel Attention Module (CAM) that can distinguish the esophagus and surrounding tissues by emphasizing and inhibiting channel feature and Cross-level Feature Fusion Module (CFFM) which is utilized to strengthen the generalization ability of the network by using high-level features to weight low-level features. Because the high-level features represent specific organizational information, and the low-level features represent the characteristics of detailed information such as edges and contours, the network can learn specific detailed features of a definite organization. In addition, to locate the esophageal region better, a 3D semi-automatic method for segmenting esophagus and esophageal cancer is proposed. The proposed network is trained using 46,400 CT pictures as the training set and divides 11,600 CT images from the dataset at a ratio of 0.2 as the validation set. Finally, 7,250 CT images were used as the test set to test the performance of the network. The experimental results show that the IoU value of our network can reach 0.625, the dice value is 0.732 and the Hausdorff distance is 3.193.
Highlights
Esophageal cancer is one of the most common cancers worldwide, and their incidence has been increasing in recentThe associate editor coordinating the review of this manuscript and approving it for publication was Anandakumar Haldorai .years [1]
In the three major indicators Intersection over Union (IoU), Dice Value (DV) and Hausdorff Distance (HD), we have outperformed over the state-of-the-art methods
In order to solve the problem that the network does not have high ability to segment ambiguous boundaries between different organizations in Computed Tomography (CT) images, we explicitly model the interdependence between feature channels in U-Net, and design Channel-attention U-Net which embed a Channel Attention Module (CAM) in the skip connection layer of U-Net
Summary
Esophageal cancer is one of the most common cancers worldwide, and their incidence has been increasing in recentThe associate editor coordinating the review of this manuscript and approving it for publication was Anandakumar Haldorai .years [1]. Esophageal cancer is one of the most common cancers worldwide, and their incidence has been increasing in recent. Early diagnosis and treatment are the keys to improving survival rates, and medical imaging technology has provided great help to this. Among many different imaging methods, Computed Tomography (CT) images are widely used for the diagnosis of esophagus diseases because they can provide relatively high-resolution anatomical. G. Huang et al.: Channel-Attention U-Net: Channel Attention Mechanism information. Even professional doctors can not accurately point out the areas of the esophagus and esophageal cancer. Because of the need for accurate and effective tumor mapping, the development of semi-automatic or automatic tumor accurate methods is urgent
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