In recent years, with the advanced development of smart cities, the integration of unmanned aerial vehicles (UAVs) has become crucial due to their numerous benefits across various fields, such as traffic management, surveillance operations, delivery services, emergency response, and agricultural monitoring. This study explores the diverse types and applications of UAVs used in smart cities to enhance the quality of life for their residents. It also focuses on the role of UAVs in improving the efficiency and functionality of these cities. By using UAVs, cities can achieve improved traffic flow, better emergency response capabilities, and more efficient surveillance systems. However, this integration is not without challenges. Technical limitations include issues related to navigation, communication, speed, and privacy. Additionally, non-technical limitations such as safety concerns, privacy issues, and the costs associated with UAV development and construction pose significant challenges. Despite these challenges, advancements in UAV technology promise a future of autonomous, intelligent applications that can significantly enhance urban living.

Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.

Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which remains a costly and difficult task. Thus, the use of an unsupervised segmentation method could facilitate the process. In this article, we propose two approaches for the semantic segmentation of breast cancer histopathology images. On the one hand, an autoencoder architecture for unsupervised segmentation is proposed, and on the other hand, an improvement U-Net architecture for supervised segmentation is proposed. We evaluate these models on a public dataset of histological images of breast cancer. In addition, the performance of our segmentation methods is measured using several evaluation metrics such as accuracy, recall, precision and F1 score. The results are competitive with those of other modern methods.