Segmentation Trachea and Bronchial Branches in Chest Computed Tomography Image by Deep Learning - preliminary results -

Segmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about the required body part. However, segmenting medical images presents a number of challenges, including the need for medical professionals to be trained, the fact that it is time-consuming and prone to errors. As a result, it appears that an automated medical image segmentation system is required. Deep learning algorithms have recently demonstrated superior performance for segmentation tasks, particularly semantic segmentation networks that provide a pixel-level understanding of images. U- Net for image segmentation is one of the modern complex networks in the field of medical imaging; several segmentation networks have been built on its foundation with the advancements of Recurrent Residual convolutional units and the construction of recurrent residual convolutional neural network based on U-Net (R2U-Net). R2U-Net is used to perform trachea and bronchial segmentation on a dataset of 36,000 images. With a variety of experiments, the proposed segmentation resulted in a dice-coefficient of 0.8394 on the test dataset. Finally, a number of research issues are raised, indicating the need for future improvements.

Gold Nanoparticle Effects on the Hounsfield Unit of Computed Tomography in Hepatocellular Carcinoma: Systematic Review

Background: The present study has attempted to gather all the original and relevant data on the application of gold nanoparticles aimed at the improvement of computed tomography image quality and Hounsfield unit in hepatocellular carcinoma. We performed a systematic review on the studies indexed in PubMed from January 2000 to January 2020. Afterwards, the study design and quality were evaluated. Results: An increase in the nanoparticles concentration and incubation time was associated with improved image quality and the Hounsfield Unit of computed tomography. Conclusion: This study highlights the considerable diagnostic role of gold nanoparticle as novel contrast agents in the early detection of hepatocellular carcinoma.

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor

This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P < 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.

Age-Dependent Changes in Bone Architecture, Patterning, and Biomechanics During Skeletal Regeneration

Mouse digit amputation provides a useful model of bone growth after injury, in that the injury promotes intramembranous bone formation in an adult animal. The digit tip is composed of skin, nerves, blood vessels, bones, and tendons, all of which regenerate after digit tip amputation, making it a powerful model for multi-tissue regeneration. Bone integrity relies upon a balanced remodeling between bone resorption and formation, which, when disrupted, results in changes to bone architecture and biomechanics, particularly during aging. In this study, we used recently developed techniques to evaluate bone patterning differences between young and aged regenerated bone. This analysis suggests that aged mice have altered trabecular spacing and patterning and increased mineral density of the regenerated bone. To further characterize the biomechanics of regenerated bone, we measured elasticity using a micro-computed tomography image-processing method combined with nanoindentation. This analysis suggests that the regenerated bone demonstrates decreased elasticity compared with the uninjured bone, but there is no significant difference in elasticity between aged and young regenerated bone. These data highlight distinct architectural and biomechanical differences in regenerated bone in both young and aged mice and provide a new analysis tool for the digit amputation model to aid in evaluating the outcomes for potential therapeutic treatments to promote regeneration.