Jun 19, 2019 · In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice.

U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice by using a skip connection operation that is similar to convolution ...

The effective segmentation and 3-D rendering of esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing ...

Jul 10, 2019 · The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer. INDEX TERMS Esophageal ...

Jul 17, 2023 · Several studies have developed specific deep learning-based models relying on a U-Net convolutional architecture for automatic segmentation of ...

U-Net Plus is proposed to segment oesophagus and oesophagal cancer from a 2-D CT slice. In the new network architecture, two blocks enhance the feature ...

A novel U-Net structure that can distinguish the esophagus and surrounding tissues by emphasizing and inhibiting channel feature and Cross-level Feature Fusion ...

This work proposes a deep learning approach based on the U-Net++ method to segment EEC in GIE images. A total of 2690 GIE images collected from 617 patients.

We propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images.

Missing: Tomography | Show results with:Tomography