Bone age recognition based on mask R-CNN using xception regression model.

Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy. Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features. Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods. Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer. METHODS: The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (Ktrans ), rate constant (kep ), the volume of the extravascular extracellular space (ve ), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve. RESULTS: The DCE-MRI parameters Ktrans and kep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the Ktrans, kep , and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the Ktrans, kep , and ve (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = -0.525; p < 0.05). CONCLUSION: The DCE-MRI quantitative parameters Ktrans and kep , as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

Effects of ultrasound on vascular endothelial growth factor in cartilage, synovial fluid, and synovium in rabbit knee osteoarthritis.

Ultrasound is commonly used to treat knee osteoarthritis (KOA), which has unique advantages with regard to relieving pain and inflammation as well as delaying cartilage degeneration, but the underlying mechanisms are less clear. The study aimed to investigate the therapeutic effects of ultrasound on vascular endothelial growth factor (VEGF) expression in cartilage, the synovium, and synovial fluid (SF) in a rabbit model of KOA. Twenty-four New Zealand rabbits were randomly divided into ultrasound (group A), sham ultrasound (group B) and no-ACLT control groups (group C). Six weeks after undergoing anterior cruciate ligament transection (ACLT), group A was treated with ultrasound and group B was treated with sham ultrasound. Two weeks thereafter, the morphology of the synovium and cartilage were observed. Cartilage and synovium were scored using the Mankin scale and Krenn V scores, respectively. VEGF expression in the cartilage, SF, and synovium of ACLT knee joints was analyzed via immunohistochemistry, western blotting, and RT-PCR. Cartilage degeneration and synovitis were the most severe in group B and the least severe in group C. Similarly, Mankin scores and Krenn V scores were highest in group B and lowest in group C (p<0.05). There were also significant differences in the VEGF IOD of cartilage or synovium, VEGF protein content in SF, and VEGF mRNA expression in cartilage or SF (p<0.05). Ultrasound can relieve synovitis and delay cartilage degradation, and the mechanisms of ultrasound for the treatment of KOA may involve inhibition of the expression of VEGF in the synovium, SF, and cartilage.