Automatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.

With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia, and spine. The use of ultrasound allows data for bone surface to be acquired non-invasively and without radiation.

Automatic virtual reduction of unilateral zygomatic fractures based on ICP algorithm: A preliminary study.

Objective: To establish an automatic reduction method for unilateral zygomatic fractures based on Iterative Closes Point (ICP) algorithm.

Material And Methods: 60 patients with unilateral type B zygomatic fractures were included. After acquiring CT images, zygomatic fragments were segmented using self-developed software MICSys.

Flavonoids of Euphorbia hirta inhibit inflammatory mechanisms via Nrf2 and NF-κB pathways.

Euphorbia hirta has anti-inflammatory effects in traditional medicine, but its anti-inflammatory mechanism has not been explored at the cellular and molecular levels. To unravel these mechanisms, the main active components in the 65 and 95% ethanol extracts of Euphorbia hirta were first identified by UPLC-Q-TOF/MS. Subsequently, potential anti-inflammatory targets and signaling pathways were predicted using network pharmacology and experimentally validated using RT-PCR and flow cytometry in a lipopolysaccharide (LPS)-induced inflammation model of RAW264.

UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration.

Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can effectively utilize local information to reduce redundancy via small neighborhood convolution, the limited receptive field results in the inability to capture global dependencies.

Super-resolution landmark detection networks for medical images.

Craniomaxillofacial (CMF) and nasal landmark detection are fundamental components in computer-assisted surgery. Medical landmark detection method includes regression-based and heatmap-based methods, and heatmap-based methods are among the main methodology branches. The method relies on high-resolution (HR) features containing more location information to reduce the network error caused by sub-pixel location.