A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma.

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video.

ConMLP: MLP-Based Self-Supervised Contrastive Learning for Skeleton Data Analysis and Action Recognition.

Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approaches are based on extracting skeleton sequences through convolutional operations.

IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the difficulty of fully automatically segmenting 2D ultrasound images. The previous research has addressed this challenge using convolutional neural networks (CNN), which fails to consider global contextual information and long-range dependency.

Placental Super Micro-vessels Segmentation Based on ResNeXt with Convolutional Block Attention and U-Net.

Accurate placenta super micro-vessels segmentation is the key to diagnose placental diseases. However, the current automatic segmentation algorithm has issues of information redundancy and low information utilization, which reduces the segmentation accuracy. To solve this problem, we propose a model based on ResNeXt with convolutional block attention module (CBAM) and UNet (RC-UNet) for placental super micro-vessels segmentation.

Lattice-Boltzmann interactive blood flow simulation pipeline.

Purpose: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges.

Masculinity, Injury, and Death: Implications for Anti-Knife-Carrying Messages.

Although knives are the most common homicide instrument in Britain, factors that influence knife-carrying tolerance (i.e., the extent to which it is seen as acceptable and justified) and perceptions of anti-knife messages (i.

Effect of crosslinking in cartilage-like collagen microstructures.

The mechanical performance of biological tissues is underpinned by a complex and finely balanced structure. Central to this is collagen, the most abundant protein in our bodies, which plays a dominant role in the functioning of tissues, and also in disease. Based on the collagen meshwork of articular cartilage, we have developed a bottom-up spring-node model of collagen and examined the effect of fibril connectivity, implemented by crosslinking, on mechanical behaviour.

A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements.

Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. The high dimensionality of spectral data, however, presents a major challenge for analysis. Here, we present a second-derivative Beer's law-based technique aimed at projecting spectral data onto a lower dimension feature space characterised by the constituents of the target tissue type.

Using a passive coordinate measurement arm for motion tracking of a rigid endoscope for augmented-reality image-guided surgery.

Background: One of the main sources of error in commercial surgical navigation systems is the tracking of surgical tools. Mainstream systems typically use optical or electromagnetic tracking technologies, which exhibit accuracies of the order of 1 mm. The objective of this study was to introduce a lightweight high-precision passive coordinate measurement arm into an augmented reality-based surgical navigation system to track a rigid endoscope.