Search

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation. [ABSTRACT FROM AUTHOR]

Large-scale measurement plays an increasingly important role in intelligent manufacturing. However, existing instruments have problems with immersive experiences. In this paper, an immersive positioning and measuring method based on augmented reality is introduced. An inside-out vision measurement approach using a multi-camera rig with non-overlapping views is presented for dynamic six-degree-of-freedom measurement. By using active LED markers, a flexible and robust solution is delivered to deal with complex manufacturing sites. The space resection adjustment principle is addressed and measurement errors are simulated. The improved Nearest Neighbor method is employed for feature correspondence. The proposed tracking method is verified by experiments and results with good performance are obtained. [ABSTRACT FROM AUTHOR]