Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images.

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD.

Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images.

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation.

Molecular and physiological mechanisms of tea (Camellia sinensis (L.) O. Kuntze) leaf and root in response to nitrogen deficiency.

Background: As an economically important crop, tea is strongly nitrogen (N)-dependent. However, the physiological and molecular mechanisms underlying the response of N deficiency in tea are not fully understood. Tea cultivar "Chunlv2" [Camellia sinensis (L.

CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading.

Diabetic retinopathy (DR) grading is primarily performed by assessing fundus images. Many types of lesions, such as microaneurysms, hemorrhages, and soft exudates, are available simultaneously in a single image. However, their sizes may be small, making it difficult to differentiate adjacent DR grades even using deep convolutional neural networks (CNNs).