The effective segmentation and 3-D rendering of the esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing esophageal cancer. Irregular and vague boundary causes great difficulty in the segmentation of esophagus and esophageal cancer. In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice. In the new network architecture, two blocks are employed to enhance the feature extraction performance of complex abstract information, which can effectively resolve irregular and vague boundaries. A block is a skip connection operation that is similar to convolution. The architecture is trained through a dataset of 1924 slices from 10 CT scans and tested through 295 slices from 6 CT scans. The training and test datasets are expanded tenfold to simulate the segmentation of the 3-D CT image. Using the new framework, we report a 0.79 ± 0.20 dice value and 5.87 ± 9.91 Hausdorff distance. A semi-automatic scheme is then designed for the 3-D segmentation of the esophagus or esophageal cancer. The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer.INDEX TERMS Esophageal cancer, image segmentation, deep learning, computed tomography (CT).

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In this work, ether oxide (EO)-based multilayer composite membranes were prepared via interfacial polymerization (IP) of trimesoyl chloride (TMC) and polyetheramine (PEA) on polydimethylsiloxane precoated polysulfone support membrane. The effects of preparation parameters, such as monomer concentrations, reaction time, and heat-treatment temperature on the membrane performance were investigated. The optimal preparation parameters have been concluded. The results showed the increasing monomers concentration of both PEA and TMC can lead to the decrease of CO 2 permeance and increase of CO 2 /N 2 selectivity. The optimal monomers concentration was found. When monomer concentrations are higher than the optimal values, the CO 2 permeance decreases continually while CO 2 /N 2 selectivity only shows a very limited improvement with the further increase of monomers concentration. The reaction time has similar effects on membrane performance as the monomers concentration. The effect of heat-treatment temperature was also studied. With the increasing heat-treatment temperature, the CO 2 permeance shows a decrease tendency, while the CO 2 /N 2 selectivity shows a maximum at 80 C. When PEA is 0.013 mol L −1 , TMC is 0.020 mol L −1 , reaction time is 3 min, and heat-treatment temperature is 80 C, the optimum preparation conditions are achieved with CO 2 permeance of 378.3 gas permeation unit (GPU) and CO 2 /N 2 selectivity of 51.7 at 0.03 MPa. This work may help to design and fabricate gas separation membranes with desired performance.

Surface lattice reconstruction enhanced the photoresponse performance of a self-powered ZnO NRAs/Si heterojunction UV-vis-NIR broadband photodetector.