SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation.

Automated segmentation of liver tumors in CT scans is pivotal for diagnosing and treating liver cancer, offering a valuable alternative to labor-intensive manual processes and ensuring the provision of accurate and reliable clinical assessment. However, the inherent variability of liver tumors, coupled with the challenges posed by blurred boundaries in imaging characteristics, presents a substantial obstacle to achieving their precise segmentation. In this paper, we propose a novel dual-branch liver tumor segmentation model, SBCNet, to address these challenges effectively. Specifically, our proposed method introduces a contextual encoding module, which enables a better identification of tumor variability using an advanced multi-scale adaptive kernel. Moreover, a boundary enhancement module is designed for the counterpart branch to enhance the perception of boundaries by incorporating contour learning with the Sobel operator. Finally, we propose a hybrid multi-task loss function, concurrently concerning tumors' scale and boundary features, to foster interaction across different tasks of dual branches, further improving tumor segmentation. Experimental validation on the publicly available LiTS dataset demonstrates the practical efficacy of each module, with SBCNet yielding competitive results compared to other state-of-the-art methods for liver tumor segmentation.

A multiple catheter tips tracking method in X-ray fluoroscopy images by a new lightweight segmentation network and Bayesian filtering.

During percutaneous coronary intervention, the guiding catheter plays an important role. Tracking the catheter tip placed at the coronary ostium in the X-ray fluoroscopy sequence can obtain image displacement information caused by the heart beating, which can help dynamic coronary roadmap overlap on X-ray fluoroscopy images. Due to a low exposure dose, the X-ray fluoroscopy is noisy and low contrast, which causes some difficulties in tracking. In this paper, we developed a new catheter tip tracking framework. First, a lightweight efficient catheter tip segmentation network is proposed and boosted by a self-distillation training mechanism. Then, the Bayesian filtering post-processing method is used to consider the sequence information to refine the single image segmentation results. By separating the segmentation results into several groups based on connectivity, our framework can track multiple catheter tips. The proposed tracking framework is validated on a clinical X-ray sequence dataset.

Diosgenin inhibits the expression of NEDD4 in prostate cancer cells.

Prostate cancer is the second most common malignancy among men and causes a myriad of health problem for males that are diagnosed with the cancer. Although the 5-year relative survival rate of prostate cancer patients has been significantly increased due to prostate-specific antigen testing and treatment advances, patients that develop metastatic castrate-resistant prostate cancer continue to have poor survival rates. Thus, it is critical to discover new therapeutics to treat prostate cancer. Diosgenin is a steroidal saponin from Trigonella foenum graecum, which has been previously identified to exert anti-tumor properties. Neural precursor cell expressed developmentally down-regulated protein 4 (NEDD4) is an E3 ligase that degrades multiple different proteins, and plays an oncogenic role in human cancer. In this study, we explore the molecular mechanism by which diosgenin mediates anti-tumor effects in prostate cancer cells. We found that diosgenin treatment led to cell growth inhibition, apoptosis and cell cycle arrest. Notably, we found that diosgenin inhibited the expression of NEDD4 in prostate cancer cells. Furthermore, overexpression of NEDD4 overcame the diosgenin-mediated anti-tumor activity, while downregulation of NEDD4 promoted the diosgenin-induced anti-cancer function in prostate cancer cells. Our findings indicate that diosgenin is a potential new inhibitor of NEDD4 in prostate cancer cells.

[A new method for infering vessel structure based on circle detection and Gabor filter].

To automatically infer the patterns of vessel structure such as the distal ends, segments, bifurvessel structures, and crossing of two vessels in X-ray angiographic images, a novel method is presented based on Gabor filter and circle detector. The method can cope with varying vessel curvature and intensity feature occur along the longitudinal vessel direction. The present study can facilitate 2-D quantitative description of vessel tree and 3-D vessel reconstruction, and provide an elementary clue for the diagnostics. The proposed method has been successively applied to both synthetic images for validation purposes and the actual angiographic images, which yielded encouraging results.

[Dynamic contour tracking of medical images based on improved particle filter].

In the research of medical image processing, motion estimation and tracking relating to the region of interest has been given considerable attention. For improving the quality of the noisy or cluttered medical images, the particle filter (PF) based on the non-linear and non-Gaussian Bayesian State Estimation is a better as well as a technically challenging solution. As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF, we propose in this paper a better algorithm for its improvement; in addition, to ensure better tracking of the dynamic contour with the PF, we proposed a new algorithm for the likelihood and prior probability density. Objective theoretical evaluation and substantial comparative experiments suggest that this method can be a good solution for accurate dynamic contour tracking.