CANet: Context aware network with dual-stream pyramid for medical image segmentation

Xie, Xiwang; Zhang, Wensheng; Pan, Xipeng; Xie, Lijie; Shao, Feng; Zhao, Wenyi; J, An

doi:10.1016/j.bspc.2022.104437

Cited by 37 publications

(11 citation statements)

References 24 publications

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“…Some parents believe that doing homework is more important than sports, which directly leads to the problem of insufficient physical activity of children. The World Health Organization pays close attention to the situation of physical inactivity, and released the Global Action Plan for Promoting Physical Activity from 2018 to 2030 in 2018, where in it set the goal of reducing the proportion of physical inactivity by 15% by 2030 [6]. In view of the serious shortage of moderate-to-vigorous physical activity time for urban migrant children, timely intervention and guidance should be given to ensure the healthy development of vulnerable children.…”

Section: Discussionmentioning

confidence: 99%

“…In 2018, the Sports Guide for Preschool Children (3-6 years old) (Expert Consensus Version), which was jointly developed by the Beijing Sport University of China, the Capital Institute of Pediatrics, and the General Administration of Sport of the People's Republic of China, also suggested that children should spend no less than 60 min of physical activity with moderate or higher intensity throughout the day [5]. At present, the survey data shows that as many as 81.0% of adolescents and children are physically inactive globally [6], and only 20% of children meet the daily moderate intensity physical activity requirements recommended by the World Health Organization [7]. The results of the research on outdoor physical activity of children in Guangdong, China show that only 15.6% of children meet the requirements of moderate-to-vigorous physical activity [4], which is lower than the general level.…”

Section: Introductionmentioning

confidence: 99%

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Influencing Factors of Sports Activities of Urban Migrant Children Based on Intelligent Evaluation

Wang

Jin

2023

Sustainability

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In this paper, a cluster sampling method was used to randomly select 1308 young children from the southern part of Fujian Province, China, and the short format of the International Physical Activity Questionnaire (IPAQ) was used to investigate their physical activity. Logistic regression was used to analyze the influencing factors of children’s moderate-to-vigorous physical activity (MVPA) time. Results: The MVPA time deficit rate of urban migrant children in southern Fujian was 90.1%, and that of girls (92.2%) was significantly higher than that of boys (88.5%); Multivariate logistic regression analysis found that gender, parents’ support on sports, and sports grounds near their homes were the main factors affecting the lack of MVPA time for urban migrant children.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influencing Factors of Sports Activities of Urban Migrant Children Based on Intelligent Evaluation

Wang

Jin

2023

Sustainability

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“…The neural network introduces spatial information, which solves the problem of unbalanced pixel distribution and noise in the image, and is very suitable for segmenting abdominal CT images. Xie et al [17] designed a context-aware network with dual-stream pyramid for medical image segmentation. Budak et al [18] trained dual-branch encoder-decoder networks (EDCNN) for liver and cancer segmentation.…”

Section: Data-driven-based Methodsmentioning

confidence: 99%

“…Xie et al. [17] designed a context‐aware network with dual‐stream pyramid for medical image segmentation. Budak et al.…”

Section: Related Workmentioning

confidence: 99%

MDR‐Net: Multiscale dense residual networks for liver image segmentation

Xie

Zhu

2023

IET Image Processing

Self Cite

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Liver image segmentation is an attractive topic in the diagnosis and surgical planning of liver diseases. Although deep learning methods have significantly advanced liver segmentation, existing frameworks fail to clearly determine liver boundaries, especially in medical images where various organs have similar grey levels. In this paper, the authors design a multi-scale dense residual network (MDR-Net) for liver segmentation, which consists of two blocks: a liver segmentation network and an edge-aware network. In the segmentation network, the authors introduce a multi-scale residual pooling module combining channel attention (CA) mechanism and depth-wise separable convolution to accommodate liver scale variation. Furthermore, the authors employ an edge-aware loss network to refine edge information and enhance feature representation, which is beneficial to guide the network to iterate towards the ground truth. The authors' method achieves the best visualization results in qualitative evaluation. In addition, the authors' method achieves 96.189% on 3D-IRCADb and 96.889% on the CHAOS dataset in quantitative evaluation with respect to the dice index.

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“…The network is designed to extract useful information from various medical images by combining these modules. The proposed network aims to improve medical image segmentation accuracy by leveraging the benefits of these different modules [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of children’s oral diagnosis and treatment using imaging examination using AI based Internet of Things

Li,

Qu,

Yue

et al. 2024

THC

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BACKGROUND: Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children’s dental fear (CDF). OBJECTIVE: This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF. METHODS: Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n= 38) and controls (n= 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children’s fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery. RESULTS: The results showed that the S⁢A value of CV-LBF algorithm was higher than that of region growing algorithm (P< 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P< 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P< 0.001), and Venham scores were lower in GAG than in controls after intervention (P< 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P< 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P< 0.001). CONCLUSION: The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children’s teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals.

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CANet: Context aware network with dual-stream pyramid for medical image segmentation

Cited by 37 publications

References 24 publications

Influencing Factors of Sports Activities of Urban Migrant Children Based on Intelligent Evaluation

Influencing Factors of Sports Activities of Urban Migrant Children Based on Intelligent Evaluation

MDR‐Net: Multiscale dense residual networks for liver image segmentation

Evaluation of children’s oral diagnosis and treatment using imaging examination using AI based Internet of Things

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