Volume 1, Issue 3

The pandemic virus COVID-19 has caused hundreds of millions of infections and deaths, resulting in enormous social and economic losses worldwide. As the virus strains continue to evolve, their ability to spread increases. The detection by reverse transcription polymerase chain reaction is time-consuming and less sensitive. As a result, X-ray images and computed tomography images started to be used in the diagnosis of COVID-19. Since the global outbreak, medical image processing researchers have proposed several automated diagnostic models in the hope of helping radiologists and improving diagnostic accuracy. This paper provides a systematic review of these diagnostic models from three aspects: image preprocessing, image segmentation, and classification, including the common problems and feasible solutions that encountered in each category. Furthermore, commonly used public COVID-19 datasets are reviewed. Finally, future research directions for medical image processing in managing COVID-19 are proposed.

Medical ultrasound imaging is an integral part of preoperative diagnosis, lesion screening and ultrasound-guided interventional surgeries. Image segmentation techniques can enhance the identification of lesions and separate them from complex backgrounds, aiding physicians in both quantitative and qualitative analyses. Ultrasound image segmentation algorithms are primarily categorized into two types: traditional non-semantic segmentation and deep learning-based semantic segmentation, each with distinct advantages and drawbacks. This paper delves into these segmentation principles, elucidating their relevance in the realm of ultrasound image segmentation, and offers an overview of current research trends. Our goal is to provide guidance for physicians and researchers in selecting the most suitable segmentation algorithm that tailors to their specific requirements.

Purpose: To explore the effect of electrode structure on welding quality by modifying the structure of magnesium alloy electrode. Methods: Two novel electrodes were designed in this study, including one with a circle (C) surface and the other with a square and arched (SA) surface. The designed electrodes were compared to a control electrode with a smooth surface in terms of temperature distribution, thermal damage to tissue, and effective welding area. Finite element analysis was used to analyze the stress and strain of all electrodes and thermal damage to the tissue. Results: Pressure applied to the designed electrodes was within the elastic limit, and the deformation was less than 1%. The highest temperature of SA electrode (99.6 ℃) was similar to that of the control (100 ℃), while that of C electrode (106 ℃) was higher than the control. The mean temperature at the welding site in intestine of the control electrode was significantly higher than that of the other two electrodes. Besides, the mean temperature of C electrode was also slightly higher than that of SA electrode. The tissues welded by the control electrode, C electrode and SA electrode were completely necrotic within an axial distance of 2.546 mm, 2.079 mm, and 1.835 mm from the edge of the welding area, respectively. Conclusion: SA electrode has the lowest thermal damage compared with the other two electrodes due to smaller effective welding area. Therefore, SA electrode is better than the other two electrodes.

Background: Irreversible electroporation (IRE) is an emerging tissue ablation technique that offers advantages over traditional catheter ablation, such as minimal thermal damage and reduced treatment time. However, as this technique also involves delivering energy through a catheter to target tissue, there are still challenges regarding the contact between the catheter and the targeted tissue, and there is a lack of relevant studies. In this study, we examined this issue using potato models with three groups of experiments. Methods: First, the relationship between the effect of biphasic and monophasic output modes and contact force (CF) was studied. Next, the effect of different voltages on biphasic output mode was examined. Finally, impedance analysis was conducted to test the contact impedance under different CFs. Results: The IRE ablation efficacy increased with the increase of CF in both monophasic and biphasic output modes, and there was a strong correlation between the ablation efficacy and the CF. In addition, at three voltage levels, the IRE ablation efficacy increased with increasing CF, and there was a strong correlation between the ablation efficacy and the CF. Conclusion: The results indicate that, under common IRE electrical parameter configuration, the effect of IRE on the tissue has a positive response to the CF of the electrode in the potato model. This finding has important implications for the design of electrodes used in IRE for the treatment of atrial fibrillation.

In contrast to conventional oximeters, near-infrared spectroscopy-based brain tissue oximetry monitoring devices are capable of non-invasive, continuous, and real-time quantitative monitoring of cerebral oximetry parameters. Initially, these devices were utilized for intensive care or surgical monitoring of oxygen saturation. Due to the rapid advancement of optoelectronic sensing and measurement technologies over the past decade, the derived functional near-infrared brain imaging devices have been widely used in a variety of fields. This paper first introduces the basic principles of near-infrared spectroscopy-based cerebral oxygenation parameter detection, then focuses on the most recent developments in this field of study. Finally, a prospect on their future application in practical settings is also provided.