Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer

Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%

Prognostic value of the ascites characteristics in pseudomyxoma peritonei originating from the appendix

BackgroundPseudomyxoma peritonei (PMP) is a rare disease, with the overall survival (OS) influenced by many factors. To date, no ascites characteristics have been reported to predict OS of patients with PMP. The present study therefore aims to describe the ascites characteristics for PMP and identify prognostic factors for survival.MethodsBetween June 2010 and June 2020, 473 PMP patients who underwent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy were included in a retrospective study. Survival analysis was performed with the Kaplan–Meier method by the log-rank test and a Cox proportional hazards model. Associations between categorical variables were analyzed using the chi-squared test.ResultsAmong all included patients, 61% were women. The median OS was 47 months (range, 4–124 months) at the last follow-up in December 2020. Ascites characteristics can be divided into light blood ascites, “Jelly” mucus ascites, and faint yellow and clear ascites. Multivariate Cox analysis showed that the degree of radical surgery, ascites characteristics, and pathological grade were independently associated with OS in PMP patients. The chi-squared test documented that faint yellow “Jelly” ascites were related to low-grade PMP and light blood ascites were associated with high-grade PMP (P < 0.01).ConclusionsLight blood ascites, incomplete cytoreduction surgery, and high-grade histopathology may predict poor OS in appendix-derived PMP

Mechanism of Bazhen decoction in the treatment of colorectal cancer based on network pharmacology, molecular docking, and experimental validation

ObjectiveBazhen Decoction (BZD) is a common adjuvant therapy drug for colorectal cancer (CRC), although its anti-tumor mechanism is unknown. This study aims to explore the core components, key targets, and potential mechanisms of BZD treatment for CRC.MethodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) was employed to acquire the BZD’s active ingredient and targets. Meanwhile, the Drugbank, Therapeutic Target Database (TTD), DisGeNET, and GeneCards databases were used to retrieve pertinent targets for CRC. The Venn plot was used to obtain intersection targets. Cytoscape software was used to construct an “herb-ingredient-target” network and identify core targets. GO and KEGG pathway enrichment analyses were conducted using R language software. Molecular docking of key ingredients and core targets of drugs was accomplished using PyMol and Autodock Vina software. Cell and animal research confirmed Bazhen Decoction efficacy and mechanism in treating colorectal cancer.ResultsBZD comprises 173 effective active ingredients. Using four databases, 761 targets related to CRC were identified. The intersection of BZD and CRC yielded 98 targets, which were utilized to construct the “herb-ingredient-target” network. The four key effector components with the most targets were quercetin, kaempferol, licochalcone A, and naringenin. Protein-protein interaction (PPI) analysis revealed that the core targets of BZD in treating CRC were AKT1, MYC, CASP3, ESR1, EGFR, HIF-1A, VEGFR, JUN, INS, and STAT3. The findings from molecular docking suggest that the core ingredient exhibits favorable binding potential with the core target. Furthermore, the GO and KEGG enrichment analysis demonstrates that BZD can modulate multiple signaling pathways related to CRC, like the T cell receptor, PI3K-Akt, apoptosis, P53, and VEGF signaling pathway. In vitro, studies have shown that BZD dose-dependently inhibits colon cancer cell growth and invasion and promotes apoptosis. Animal experiments have shown that BZD treatment can reverse abnormal expression of PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53 genes. BZD also increases the ratio of CD4+ T cells to CD8+ T cells in the spleen and tumor tissues, boosting IFN-γ expression, essential for anti-tumor immunity. Furthermore, BZD has the potential to downregulate the PD-1 expression on T cell surfaces, indicating its ability to effectively restore T cell function by inhibiting immune checkpoints. The results of HE staining suggest that BZD exhibits favorable safety profiles.ConclusionBZD treats CRC through multiple components, targets, and metabolic pathways. BZD can reverse the abnormal expression of genes such as PI3K, AKT, MYC, EGFR, HIF-1A, VEGFR, JUN, STAT3, CASP3, and TP53, and suppresses the progression of colorectal cancer by regulating signaling pathways such as PI3K-AKT, P53, and VEGF. Furthermore, BZD can increase the number of T cells and promote T cell activation in tumor-bearing mice, enhancing the immune function against colorectal cancer. Among them, quercetin, kaempferol, licochalcone A, naringenin, and formaronetin are more highly predictive components related to the T cell activation in colorectal cancer mice. This study is of great significance for the development of novel anti-cancer drugs. It highlights the importance of network pharmacology-based approaches in studying complex traditional Chinese medicine formulations

Fine-tuning of microglia polarization prevents diabetes-associated cerebral atherosclerosis

Diabetes increases the occurrence and severity of atherosclerosis. When plaques form in brain vessels, cerebral atherosclerosis causes thickness, rigidity, and unstableness of cerebral artery walls, leading to severe complications like stroke and contributing to cognitive impairment. So far, the molecular mechanism underlying cerebral atherosclerosis is not determined. Moreover, effective intervention strategies are lacking. In this study, we showed that polarization of microglia, the resident macrophage in the central nervous system, appeared to play a critical role in the pathological progression of cerebral atherosclerosis. Microglia likely underwent an M2c-like polarization in an environment long exposed to high glucose. Experimental suppression of microglia M2c polarization was achieved through transduction of microglia with an adeno-associated virus (serotype AAV-PHP.B) carrying siRNA for interleukin-10 (IL-10) under the control of a microglia-specific TMEM119 promoter, which significantly attenuated diabetes-associated cerebral atherosclerosis in a mouse model. Thus, our study suggests a novel translational strategy to prevent diabetes-associated cerebral atherosclerosis through in vivo control of microglia polarization

A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences

This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality

Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) images is an essential step for calculation of clinical indices such as stroke volume, ejection fraction. In this paper, a new automatic LV segmentation method combines a Hierarchical Extreme Learning Machine (H-ELM) and a new location method is developed. An H-ELM can achieve more compact and meaningful feature representations and learn the segmentation task from the ground truth. A new automatic LV location method is integrated to improve the accuracy of classification and reduce the cost of segmentation. Experimental results (including 30 cases, 10 cases for training, 20 cases for testing) show that the mean absolute deviation of images segmented by our proposed method is about 67.9, 81.3 and 98.7% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean maximum absolute deviation of images segmented by our proposed method is about 63.5, 77.3 and 98.0% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean dice similarity coefficient of images segmented by our proposed method is about 13.7, 9.3 and 0.5% higher than that of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean speed of our proposed method is about 38.3, 6.7 and 23.8 times faster than that of the level set, the SVM and Hu’s method, respectively. The standard deviation of our proposed method is the lowest among four methods. The results validate that our proposed method is efficient and satisfactory for the LV segmentation