LEDCNet: A Lightweight and Efficient Semantic Segmentation Algorithm Using Dual Context Module for Extracting Ground Objects from UAV Aerial Remote Sensing Images

Semantic segmentation for extracting ground objects, such as road and house, from UAV remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping field. In recent years, with the deepening of layers and boosting of complexity, the number of parameters in convolution-based semantic segmentation neural networks considerably increases, which is obviously not conducive to the wide application especially in the industry. In order to make the model lightweight and improve the model accuracy, a new lightweight and efficient network for the extraction of ground objects from UAV remote sensing images, named LEDCNet, is proposed. The proposed network adopts an encoder-decoder architecture in which a powerful lightweight backbone network called LDCNet is developed as the encoder. We would extend the LDCNet become a new generation backbone network of lightweight semantic segmentation algorithms. In the decoder part, the dual multi-scale context modules which consist of the ASPP module and the OCR module are designed to capture more context information from feature maps of UAV remote sensing images. Between ASPP and OCR, a FPN module is used to and fuse multi-scale features extracting from ASPP. A private dataset of remote sensing images taken by UAV which contains 2431 training sets, 945 validation sets, and 475 test sets is constructed. The proposed model performs well on this dataset, with only 1.4M parameters and 5.48G FLOPs, achieving an mIoU of 71.12%. The more extensive experiments on the public LoveDA dataset and CITY-OSM dataset to further verify the effectiveness of the proposed model with excellent results on mIoU of 65.27% and 74.39%, respectively. All the experimental results show the proposed model can not only lighten the network with few parameters but also improve the segmentation performance.Comment: 11 page

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion

Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. However, due to cardiac beat, blood flow, or the patient's involuntary movement during the long acquisition, the reconstructed images are prone to motion artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac cine MRI acquisition to achieve high-quality images is necessary for clinical practice. Approach: A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction under free breathing conditions. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the Motion-Guided Deformable Alignment (MGDA) method with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. Main results: At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSE of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072. Significance: The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure

Badanie wstępne ekspresji i wartości klinicznej oznaczenia czynnika wzrostu pochodzenia płytkowego BB, czynnika indukowanego hipoksją-1α i receptora chemokiny C-C typu 2 we krwi obwodowej w patogenezie choroby Gravesa-Basedowa

Introduction: Platelet-derived growth factor BB (PDGF-BB) plays an important role in the development of GD (Graves’ disease). However, it is still unknown whether PDGF-BB is expressed in peripheral blood and whether the expression of PDGF-BB contributes to GD. We aim to study the expression of PDGF-BB, hypoxia inducible factor (HIF)-1α and C-C motif chemokine receptor (CCR)-2 in peripheral blood of patients with GD and explore its effect and potential mechanism in pathogenesis. Material and methods: 41 patients with GD (GD group) and forty-five healthy people (control group) were chosen. The concentration of PDGF-BB and HIF-1α in peripheral blood specimens were detected and compared between the two groups. The expression of CCR2 in macrophages in the peripheral blood specimens were examined using FCM (Flow Cytometry). Results: Both PDGF-BB and HIF-1α were expressed in human peripheral blood from the two groups. Compared with specimens from healthy people, there were statistically increased concentrations of PDGF-BB and HIF-1α in the GD group (P < 0.05). The proportion of CCR2-positive macrophages in peripheral blood in the GD group was significantly higher than that in the control group (P < 0.05). Conclusions: CCR2-positive macrophages may induce the expression of PDGF-BB through HIF-1α signal, and the high expression of PDGF-BB may be involved in the pathogenesis of GD.Wprowadzenie: Czynnik wzrostu pochodzenia płytkowego BB (platelet-derived growth factor BB, PDGF-BB) odgrywa ważną rolę w roz­woju choroby Gravesa-Basedowa (Graves’ disease, GD). Jednak wciąż nie wiadomo, czy PDGF-BB ulega ekspresji we krwi obwodowej i czy ekspresja PDGF-BB przyczynia się do GD. Badanie przeprowadzono w celu zbadania ekspresji PDGF-BB, czynnika indukowanego hipoksją-1α (hypoxia inducible factor-1α, HIF-1α) i receptora chemokiny C-C typu 2 (C-C motif chemokine receptor-2, CCR-2) we krwi obwo­dowej pacjentów z GD i zbadania wpływu tych cząsteczek i potencjalnego mechanizmu ich działania w patogenezie choroby. Materiał i metody: Do badania włączono 41 pacjentów z GD (grupa GD) i 45 osób zdrowych (grupa kontrolna). Stężenie PDGF-BB i HIF-1α w próbkach krwi obwodowej oznaczono i porównano między grupami. Do pomiaru ekspresji CCR2 w makrofagach krwi obwodowej zastosowano metodę cytometrii przepływowej (flow cytometry, FCM). Wyniki: W obu grupach badanych stwierdzono ekspresję PDGF-BB i HIF-1α we krwi obwodowej. W grupie GD odnotowano istotne statystycznie wyższe stężenia PDGF-BB i HIF-1α niż u osób zdrowych (p &lt; 0,05). Odsetek makrofagów CCR2-dodatnich we krwi obwo­dowej w grupie GD był istotnie wyższy niż w grupie kontrolnej (p &lt; 0,05). Wnioski: Makrofagi CCR2-dodatnie mogą indukować ekspresję PDGF-BB za pośrednictwem sygnału HIF-1α, a wysoka ekspresja PDGF­BB może odgrywać rolę w patogenezie GD

The exposure-response relationship between temperature and childhood hand, foot and mouth disease: A multicity study from mainland China.

BACKGROUND: Hand, foot and mouth disease (HFMD) is a rising public health issue in the Asia-Pacific region. Numerous studies have tried to quantify the relationship between meteorological variables and HFMD but with inconsistent results, in particular for temperature. We aimed to characterize the relationship between temperature and HFMD in various locations and to investigate the potential heterogeneity. METHODS: We retrieved the daily series of childhood HFMD counts (aged 0-12 years) and meteorological variables for each of 143 cities in mainland China in the period 2009-2014. We fitted a common distributed lag nonlinear model allowing for over dispersion to each of the cities to obtain the city-specific estimates of temperature-HFMD relationship. Then we pooled the city-specific estimates through multivariate meta-regression with city-level characteristics as potential effect modifiers. RESULTS: We found that the overall pooled temperature-HFMD relationship was shown as an approximately inverted V shape curve, peaking at the 91th percentile of temperature with a risk ratio of 1.30 (95% CI: 1.23-1.37) compared to its 50th percentile. We found that 68.5% of the variations of city-specific estimates was attributable to heterogeneity. We identified rainfall and altitude as the two main effect modifiers. CONCLUSIONS: We found a nonlinear relationship between temperature and HFMD. The temperature-HFMD relationship varies depending on geographic and climatic conditions. The findings can help us deepen the understanding of weather-HFMD relationship and provide evidences for related public health decisions

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro