Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning.

Background: The limitation in spatial resolution of bone scintigraphy, combined with the vast variations in size, location, and intensity of bone metastasis (BM) lesions, poses challenges for accurate diagnosis by human experts. Deep learning-based analysis has emerged as a preferred approach for automating the identification and delineation of BM lesions. This study aims to develop a deep learning-based approach to automatically segment bone scintigrams for improving diagnostic accuracy.

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Background: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams.

[Hydrogen and Oxygen Isotopic Characteristics and Influencing Factors of "Three Waters" in Shandian River Basin].

In this study, we collected precipitation from February 2020 to February 2022 and the surface water and groundwater in the wet (August) and dry (October) periods of 2021 in the Shandian River Basin. Stable isotope technology was used to analyze the temporal and spatial changes in the hydrogen and oxygen stable isotopes of the "three waters" in the basin to explore the relationship between water isomorphs and environmental factors and to reveal the water conversion relationship using the end element mixing model. The results showed that the slope and intercept of the local precipitation line were smaller than the local atmospheric precipitation line.

Effect of vacuum sealing drainage in dermatoplasty of large area of cutaneous defects.

Objectives: To investigate the clinical effect of vacuum sealing drainage in dermatoplasty of large area of cutaneous defects in comparison to conventional treatment.

Methods: 80 patients with large area of cutaneous defects were enrolled in this study, and they had received superficial thickness dermatoplasty. Then these patients were randomly divided into two groups based on follow-up treatments: vacuum sealing drainage (defined as group A, 40 cases) and conventional treatment (defined as group B, 40 cases).