With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.
The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.
Objective To explore the source and distribution of patients with multidrug resistant organisms (MDROs) acquired (infections/colonizations) outside the hospital and to provide a reference for guiding proactive interventions for nosocomial transmission of MDROs. Methods Bacterial culture results and clinical data of patients newly admitted to Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospita1 were retrospectively investigated between January 1st 2022 and December 31st 2023. The types of MDROs infections/colonizations, patient sources, and triple distributions of patients with nosocomial acquisition of MDROs were analyzed. Results A total of 293 patients with 308 infections/colonizations were investigated in the extranocomial infection of MDROs, 198110 newly admitted patients during the same period, and the total case rate of extranocomial infection of MDROs was 0.155% (308/198110). Among them, the case rate of extranocomial infection of methicillin-resistant Staphylococcus aureus (0.062%) and carbapenem resistant Acinetobacter baumannii (0.044%) were higher than those of other types of bacteria. The case rate of extranocomial infection of MDROs was statistically significant in terms of the distribution of the route of admission, gender of the patient, age of the patient, department of admission, and time of admission (P<0.001); The distribution of patients with extranocomial infection of various types of MDROs was correlated with admission route, patient age, and admission department (P<0.001), and the associations with patient gender and admission time were not statistically significant (P>0.05). Conclusions The total case rate of extranocomial infection of MDROs in the institution was at a relatively low level, and conducting large-scale active screening has certain limitations. Active screening factors should be considered in a comprehensive manner to capture differences in epidemiological characteristics of patients with extranocomial infection of MDROs, and targeted prevention and intervention should be carried out to achieve a reduction in infections from MDROs in hospitals.
