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Background: Brain tumor segmentation plays a significant role in clinical treatment and surgical planning. Recently, several deep convolutional networks have been proposed for brain tumor segmentation and have achieved impressive performance. However, most state-of-the-art models use 3D convolution networks, which require high computational costs. This makes it difficult to apply these models to medical equipment in the future. Additionally, due to the large diversity of the brain tumor and uncertain boundaries between sub-regions, some models cannot well-segment multiple tumors in the brain at the same time., Results: In this paper, we proposed a lightweight hierarchical convolution network, called LHC-Net. Our network uses a multi-scale strategy which the common 3D convolution is replaced by the hierarchical convolution with residual-like connections. It improves the ability of multi-scale feature extraction and greatly reduces parameters and computation resources. On the BraTS2020 dataset, LHC-Net achieves the Dice scores of 76.38%, 90.01% and 83.32% for ET, WT and TC, respectively, which is better than that of 3D U-Net with 73.50%, 89.42% and 81.92%. Especially on the multi-tumor set, our model shows significant performance improvement. In addition, LHC-Net has 1.65M parameters and 35.58G FLOPs, which is two times fewer parameters and three times less computation compared with 3D U-Net., Conclusion: Our proposed method achieves automatic segmentation of tumor sub-regions from four-modal brain MRI images. LHC-Net achieves competitive segmentation performance with fewer parameters and less computation than the state-of-the-art models. It means that our model can be applied under limited medical computing resources. By using the multi-scale strategy on channels, LHC-Net can well-segment multiple tumors in the patient's brain. It has great potential for application to other multi-scale segmentation tasks., (© 2022. The Author(s).)

Background: Many Chinese elderly increasingly face the serious problem of the "empty nest" phenomenon. The elderly living independently, also called empty-nest elderly, refers to elderly people living alone whose children left home. However few studies concerned about the subjective well-being (SWB) of the elderly living independently., Methods: This study employs The Memorial University of Newfoundland Scale of Happiness (MUNSH) to explore the SWB of the elderly living independently in rural areas of Wenzhou, a relatively developed region in China. 536 sampled are randomly selected., Results: The results indicate that participants obtained low scores in positive affect, positive experience, and the total SWB score, but high scores in negative affect and negative experience. Age, low education, poor health condition and little income were found to be negatively correlated with SWB. The SWB score of the elderly living with a spouse is higher than those who divorced or lost their spouse and the score of women is lower than that of men. In addition, the survey revealed that children's support has a positive influence on the SWB of the rural elderly living independently., Conclusions: The elderly living independently in rural Wenzhou, China have unfavorable SWB. Poor socio-economic statuses are negative impact factors. But the children's support can help to improve. Special attention is needed to those with lower socio-economic status and less children's support.