Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

© 2018 The Authors Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall\u27s thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n=10) and MRI (n=10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV

Effects of Sleep Quality on the Association between Problematic Mobile Phone Use and Mental Health Symptoms in Chinese College Students

Problematic mobile phone use (PMPU) is a risk factor for both adolescents’ sleep quality and mental health. It is important to examine the potential negative health effects of PMPU exposure. This study aims to evaluate PMPU and its association with mental health in Chinese college students. Furthermore, we investigated how sleep quality influences this association. In 2013, we collected data regarding participants’ PMPU, sleep quality, and mental health (psychopathological symptoms, anxiety, and depressive symptoms) by standardized questionnaires in 4747 college students. Multivariate logistic regression analysis was applied to assess independent effects and interactions of PMPU and sleep quality with mental health. PMPU and poor sleep quality were observed in 28.2% and 9.8% of participants, respectively. Adjusted logistic regression models suggested independent associations of PMPU and sleep quality with mental health (p < 0.001). Further regression analyses suggested a significant interaction between these measures (p < 0.001). The study highlights that poor sleep quality may play a more significant role in increasing the risk of mental health problems in students with PMPU than in those without PMPU

Low physical activity and high screen time can increase the risks of mental health problems and poor sleep quality among Chinese college students.

To test the independent and interactive associations of physical activity (PA) and screen time (ST) with self-reported mental health and sleep quality among Chinese college students.Data were collected in October, 2013. The gender, age, residential background, body mass index (BMI), perceived family economy and perceived study burden were obtained from a total of 4747 college students (41.6% males and 58.4% females). The outcomes were self-reported PA status, ST, anxiety, depression, psychopathological symptoms and sleep quality. Analyses were conducted with logistic regression models.Overall, 16.3%, 15.9% and 17.3% of the students had psychological problems, such as anxiety, depression and psychopathological symptoms, respectively. The prevalence of poor sleep quality was 9.8%. High ST was significantly positively associated with anxiety (OR=1.38, 95%CI: 1.15-1.65), depression (OR=1.76, 95%CI: 1.47-2.09), psychopathological symptoms (OR=1.69, 95%CI: 1.43-2.01) and poor sleep quality (OR=1.32, 95%CI: 1.06-1.65). High PA was insignificantly negatively associated with anxiety, depression, psychopathological symptoms and poor sleep. Low PA and high ST were independently and interactively associated with increased risks of mental health problems and poor sleep quality (p<0.05 for all).Interventions are needed to reduce ST and increase PA in the lifestyles of young people. Future research should develop and measure the impacts of interventions and their potential consequences on sleep, health, and well being

Childhood emotional and behavior problems and their associations with cesarean delivery

Objective: To determine the prevalence of childhood emotional and behavioral problems and examine their associations with cesarean delivery. Methods: Our sample consisted of 8,900 preschoolers from 35 kindergartens in four cities in East China. Parents completed the Strengths and Difficulties Questionnaire (SDQ) and provided other information. Children’s emotional and behavioral problems were assessed using five subscales of the SDQ. Mode of delivery was classified as vaginal or cesarean section (CS); in sub-analyses, we divided CS into elective or emergency delivery. Logistic regression was used to examine associations. Results: A total of 1,209 (13.6%) children had a total SDQ score within abnormal range; 25.5% had peer problems within abnormal range, 9.0% had abnormal emotional symptoms, 13.9% had abnormal conduct problems, 18.9% had abnormal hyperactivity problems, and 16.2% were rated abnormal in pro-social behavior. Overall, 67.3% of the children who participated were delivered by CS. In fully adjusted analysis, CS was significantly associated with abnormal total SDQ score (OR = 1.27; 95%CI 1.10-1.46; p < 0.05) and pro-social behavior (OR = 1.27; 95%CI 1.12-1.45; p < 0.0001). No significant association was found between CS and risk of having conduct problems (OR 1.13; 95%CI 0.98-1.29), peer problems (OR 1.11; 95%CI 0.99-1.24), hyperactivity (OR 1.02; 95%CI 0.91-1.15), or emotional problems (OR 1.06; 95%CI 0.90-1.24). Conclusion: In this sample, CS was associated with risk of behavioral problems, but not with emotional problems. Further research is needed to better understand these associations

Effects of sleep quality on the association between problematic mobile phone use and mental health symptoms in Chinese college students

Problematic mobile phone use (PMPU) is a risk factor for both adolescents’ sleep quality and mental health. It is important to examine the potential negative health effects of PMPU exposure. This study aims to evaluate PMPU and its association with mental health in Chinese college students. Furthermore, we investigated how sleep quality influences this association. In 2013, we collected data regarding participants’ PMPU, sleep quality, and mental health (psychopathological symptoms, anxiety, and depressive symptoms) by standardized questionnaires in 4747 college students. Multivariate logistic regression analysis was applied to assess independent effects and interactions of PMPU and sleep quality with mental health. PMPU and poor sleep quality were observed in 28.2% and 9.8% of participants, respectively. Adjusted logistic regression models suggested independent associations of PMPU and sleep quality with mental health (p &lt; 0.001). Further regression analyses suggested a significant interaction between these measures (p &lt; 0.001). The study highlights that poor sleep quality may play a more significant role in increasing the risk of mental health problems in students with PMPU than in those without PMPU