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Despite the promising advancements of deep learning techniques in coastal aquaculture pond extraction, their capacity for large-scale mapping tasks remains relatively limited. To address this challenge, this study developed a novel deep learning framework, Dual-Branch Enhanced Network (DBCE-Net), for mapping the annual aquaculture ponds at the national scale using Sentinel-2 imagery. The DBCE-Net framework effectively mitigates the contextual information loss inherent in traditional methods and reduces classification errors by processing both down-sampled large-scale images and block images at their original resolution. The architecture comprises local feature extraction and global feature extraction, along with feature fusion and decoding. The pivotal Multi-scale Dynamic Feature Fusion (DFF) module synthesizes local and global features while incorporating complementary information, demonstrating strong robustness with smaller training areas, compared to previous methods that required a larger number of samples distributed across different regions. By applying the DBCE-Net to Sentinel-2 imagery from 2017 to 2023, we mapped the spatiotemporal distribution of coastal aquaculture ponds across all coastal counties in China, achieving an overall classification accuracy approximately 93%. The results demonstrate substantial changes in the area of coastal aquaculture ponds in China from 2017 to 2023, with the total area declining from 8970.25 km² to 8261.17 km², representing a notable decrease of 7.90%. The most pronounced reduction was observed in Shanghai, with a decrease of 38.92%, followed by Zhejiang (31.57%) and Jiangsu (19.07%). These reductions are primarily attributed to policies converting aquaculture ponds into natural wetlands. In contrast, the area of coastal aquaculture ponds in Liaoning Province slightly increased by 5.75%. This DBCE-Net demonstrates good accuracy and generalizability and is promising to further expand its application to the extraction of coastal aquaculture areas worldwide, providing important scientific value and practical significance for the global coastal aquaculture industry. Full article

The structure and topology of the pulmonary arteries is crucial to understand, plan, and conduct medical treatment in the thorax area. Due to the complex anatomy of the pulmonary vessels, it is not easy to distinguish between the arteries and veins. The pulmonary arteries have a complex structure with an irregular shape and adjacent tissues, which makes automatic segmentation a challenging task. A deep neural network is required to segment the topological structure of the pulmonary artery. Therefore, in this study, a Dense Residual U-Net with a hybrid loss function is proposed. The network is trained on augmented Computed Tomography volumes to improve the performance of the network and prevent overfitting. Moreover, the hybrid loss function is implemented to improve the performance of the network. The results show an improvement in the Dice and HD95 scores over state-of-the-art techniques. The average scores achieved for the Dice and HD95 scores are 0.8775 and 4.2624 mm, respectively. The proposed method will support physicians in the challenging task of preoperative planning of thoracic surgery, where the correct assessment of the arteries is crucial. Full article