When dentists see pediatric patients with more complex tooth development than adults during tooth replacement, they need to manually determine the patient’s disease with the help of preoperative dental panoramic radiographs. To the best of our knowledge, there is no international public dataset for children’s teeth and only a few datasets for adults’ teeth, which limits the development of deep learning algorithms for segmenting teeth and automatically analyzing diseases. Therefore, we collected dental panoramic radiographs and cases from 106 pediatric patients aged 2 to 13 years old, and with the help of the efficient and intelligent interactive segmentation annotation software EISeg (Efficient Interactive Segmentation) and the image annotation software LabelMe. We propose the world’s first dataset of children’s dental panoramic radiographs for caries segmentation and dental disease detection by segmenting and detecting annotations. In addition, another 93 dental panoramic radiographs of pediatric patients, together with our three internationally published adult dental datasets with a total of 2,692 images, were collected and made into a segmentation dataset suitable for deep learning.

Objectives: Globally, major depressive disorder (MDD) is considered to be a leading cause of disability. In this article, we aim to investigate the sex difference in global burden of MDD by year, age, and socioeconomic development, utilizing disability-adjusted life-years (DALYs).Methods: Global and national sex-specific DALY estimates caused by MDD from 1990 to 2019 and in different age groups were obtained from the Global Burden of Disease (GBD) Study 2019. Human development index (HDI) was used as an indicator of national socioeconomic development. Spearman correlation and linear regression analyses were performed to explore the relationship between national socioeconomic development and sex difference in MDD burden.Results: Sex difference in global burden of MDD persisted between 1990 and 2019, with age-standardized DALY rates being 352 among males vs. 593 among females in 1990 and 354 vs. 564 in 2019. Females had higher burden of MDD than males at the same age. Disability-adjusted life-years numbers and rates among both sexes rapidly increased with age for those aged 10–24 years, along with gradually enlarging sex difference. Age-standardized DALY rates among females were higher than that among males for each HDI-based country group (P < 0.001). National age-standardized DALY rates among both sexes were negatively related to HDI. However, female-to-male age-standardized DALY rate ratios were positively associated with HDI (Spearman r = 0.383, P < 0.001; standardized β = 0.300, P < 0.001).Conclusion: Although some improvement in sex difference in global burden of MDD has been achieved, it still persists in the past three decades, with females bearing more burden than males. To reduce sex difference in global MDD burden, more attention should be paid to young people and people in developed countries. The findings highlight the importance of making sex-specific health policy to manage mental impairment caused by MDD.

Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the manage...

Macular holes, one of the most common macular diseases, require timely treatment. The morphological changes on optical coherence tomography (OCT) images provided an opportunity for direct observation of the disease, and accurate segmentation was needed to identify and quantify the lesions. Developments of such algorithms had been obstructed by a lack of high-quality datasets (the OCT images and the corresponding gold standard macular hole segmentation labels), especially for supervised learning-based segmentation algorithms. In such context, we established a large OCT image macular hole segmentation (OIMHS) dataset with 3859 B-scan images of 119 patients, and each image provided four segmentation labels: retina, macular hole, intraretinal cysts, and choroid. This dataset offered an excellent opportunity for investigating the accuracy and reliability of different segmentation algorithms for macular holes and a new research insight into the further development of clinical research for macular diseases, which included the retina, lesions, and choroid in quantitative analyses.

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS² to automatically segment IVUS images in a user-friendly environment.