RESUMO
The harvesting of salinity gradient energy through a capacitive double-layer expansion (CDLE) technique is directly associated with ion adsorption and desorption in electrodes. Herein, we show that energy extraction can be modulated by regulating ion adsorption/desorption through water flow. The flow effects on the output energy, capacitance, and energy density under practical conditions are systematically investigated from a theoretical perspective, upon which the optimal operating condition is identified for energy extraction. We demonstrate that the net charge accumulation displays a negative correlation with the water flow velocity and so does the surface charge density, and this causes a nontrivial variation in the magnitude of output energy when water flows are introduced. When high water flows are introduced in both the charging and discharging processes, the energy extraction can be significantly reduced by 47.69-49.32%. However, when a high flow is solely exerted in the discharging process, the energy extraction can be enhanced by 12.94-14.49% even at low operation voltages. This study not only offers a comprehensive understanding of the microscopic mechanisms of surface-engineered energy extraction with water flows but also provides a novel direction for energy extraction enhancement.
RESUMO
BACKGROUND: Accurate segmentation of organs has a great significance for clinical diagnosis, but it is still hard work due to the obscure imaging boundaries caused by tissue adhesion on medical images. Based on the image continuity in medical image volumes, segmentation on these slices could be inferred from adjacent slices with a clear organ boundary. Radiologists can delineate a clear organ boundary by observing adjacent slices. PURPOSE: Inspired by the radiologists' delineating procedure, we design an organ segmentation model based on boundary information of adjacent slices and a human-machine interactive learning strategy to introduce clinical experience. METHODS: We propose an interactive organ segmentation method for medical image volume based on Graph Convolution Network (GCN) called Surface-GCN. First, we propose a Surface Feature Extraction Network (SFE-Net) to capture surface features of a target organ, and supervise it by a Mini-batch Adaptive Surface Matching (MBASM) module. Then, to predict organ boundaries precisely, we design an automatic segmentation module based on a Surface Convolution Unit (SCU), which propagates information on organ surfaces to refine the generated boundaries. In addition, an interactive segmentation module is proposed to learn radiologists' experience of interactive corrections on organ surfaces to reduce interaction clicks. RESULTS: We evaluate the proposed method on one prostate MR image dataset and two abdominal multi-organ CT datasets. The experimental results show that our method outperforms other state-of-the-art methods. For prostate segmentation, the proposed method conducts a DSC score of 94.49% on PROMISE12 test dataset. For abdominal multi-organ segmentation, the proposed method achieves DSC scores of 95, 91, 95, and 88% for the left kidney, gallbladder, spleen, and esophagus, respectively. For interactive segmentation, the proposed method reduces 5-10 interaction clicks to reach the same accuracy. CONCLUSIONS: To overcome the medical organ segmentation challenge, we propose a Graph Convolutional Network called Surface-GCN by imitating radiologist interactions and learning clinical experience. On single and multiple organ segmentation tasks, the proposed method could obtain more accurate segmentation boundaries compared with other state-of-the-art methods.