Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features. To address these two issues, we propose a ConvNet-Transformer hybrid framework named SSCFormer for accurate and versatile medical image segmentation. The intra-scale ResInception and inter-scale transformer bridge are designed to collaboratively capture the intra- and inter-scale features, facilitating the interaction of small-scale disparity information at a single stage with large-scale from multiple stages. Global attention and scaling attention are cleverly integrated from a spatial-channel-aware perspective. The proposed SSCFormer is tested on four different medical image segmentation tasks. Comprehensive experimental results show that SSCFormer outperforms the current state-of-the-art methods.

Muscle fatigue caused by using staplers affects the surgical procedures of surgeons. Compared to manual staplers, electric staplers decrease the muscle fatigue by replacing repeated gripping and releasing with automatic model. Still powered by the battery, the electric staplers are confronted with new operation fatigue due to the extra weight of the battery. The weight and manual operation of the electric stapler can cause user's muscle fatigue. To tackle the unsettled fatigue issue, the study is to propose a new design method to offset the influence of extra weight of the battery attached to the electrical stapler by changing the opening and closing of the stapler end clamp to electric control. The purpose of this study is to verify whether the design of this electric stapler can offset the impact of weight. Experiments are set up to test the feasibility of the design on 12 male Chinese surgeons. The muscle fatigue is indicated by measuring the EMG (electromyography) signal, and the stapler's working effect is indicated by measuring the end movement of the stapler. The result shows the new-designed stapler reduce the operation fatigue, counteracting the impact of the battery weight.

Objective: Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the possibility of using machine learning to evaluate CPET data for automatically classifying the CPE level of workers in high-latitude areas. Methods: A total of 120 eligible workers were selected for this cardiopulmonary exercise experiment, and the physiological data and completion of the experiment were recorded in the simulated high-latitude workplace, within which 84 sets of data were used for XGBOOST model training and36 were used for the model validation. The model performance was compared with Support Vector Machine and Random Forest. Furthermore, hyperparameter optimization was applied to the XGBOOST model by using a genetic algorithm. Results: The model was verified by the method of tenfold cross validation; the correct rate was 0.861, with a Micro-F1 Score of 0.864. Compared with RF and SVM, all data achieved a better performance. Conclusion: With a relatively small number of training samples, the GA-XGBOOST model fits well with the training set data, which can effectively evaluate the CPE level of subjects, and is expected to provide automatic CPE evaluation for selecting, training, and protecting the working population in plateau areas.

数字全息层析(HT)显微术是一种先进的无标记三维显微成像方法,可以恢复细胞等生物样品的三维折射率分布,进而实现细胞体积、干质量等形态学和生理学参数的三维可视化.HT在数字全息显微记录和再现单幅数字全息图的基础原理上,通过旋转样品或旋转照明等方式获取多角度下全息图,然后通过投影层析或衍射层析等算法重建待测样品的三维折射率分布.综述HT中光路设计与重建算法的技术研究与生物学应用进展,为HT技术的国产化发展与应用研究提供借鉴.

This article briefly introduced human factors engineering concepts and depicts the risk management process for addressing use-related hazards, explained the important role of human factors engineering in elimination or reduction of use-related hazards, and provides the general process of medical devices use-related risk study. Some advices are given to eliminate or reduce of use-related hazards of medical devices.