Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency

Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics

The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE-MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population-based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 ± 0.018, an intersection-over-union (IoU) of 0.91 ± 0.033, and 1.10 ± 1.4 in the 95-percentile of Hausdorff distance (HD95). Extensive experiments confirm the superiority of the proposed method over several state-of-the-art level set methods, with an average improvement of 0.7 in terms of HD95. It also offers an HD95 improvement of 9.5 and 3.8 over two deep neural networks based on the U-Net architecture. The accuracy improvements have been experimentally found to be more prominent on low-contrast and noisy images

Microwave-Assisted Synthesis, Biological Activity Evaluation, Molecular Docking, and ADMET Studies of Some Novel Pyrrolo [2,3-b] Pyrrole Derivatives

Novel pyrrolo [2,3-b] pyrrole derivatives were synthesized and their hypolipidemic activity was assessed in hyperlipidemic rats. The chemical structures of the new derivatives were confirmed through spectral analysis. Compounds 5 and 6 were revealed to be the most effective hypolipidemic agents, with considerable hypocholesterolemic and hypotriglyceridemic effects. They appear to be promising candidates for creating new powerful derivatives with anti-atherosclerotic and hypolipidemic properties. As for antimicrobial activity, some of the tested compounds showed moderate activity against Pseudomonas aeruginosa: compound 2 revealed an MIC value of 50 μg/mL, compared to 25 μg/mL for ciprofloxacin. Compound 3 showed good antimicrobial activity against Staphylococcus aureus, comparable to ciprofloxacin, and roughly half the activity of ampicillin, according to MIC values. Compound 2 has an MIC approximately 25% of that of clotrimazole against Candida albicans. Compound 2 also showed the highest antioxidant activity with 59% inhibition of radical scavenging activity. Additionally, the cytotoxic activity of these new derivatives 1–7 was investigated and most of them showed good anticancer activity against the three tested cell lines

Thieno[2,3‑<i>b</i>]thiophene Derivatives as Potential EGFR^WT and EGFRT^790M Inhibitors with Antioxidant Activities: Microwave-Assisted Synthesis and Quantitative In Vitro and In Silico Studies

Microwave-assisted synthesis and spectral analysis of certain novel derivatives of 3,4-diaminothieno[2,3-b]thiophene-2,5-dicarbonitrile 1–7 were carried out. Compounds 1–7 were examined for cytotoxicity against MCF-7 and A549 cell lines using the quantitative MTT method, and gefitinib and erlotinib were used as reference standards. Compounds 1–7 were shown to be more active than erlotinib against the two cell lines tested. Compound 2 outperformed regular erlotinib by 4.42- and 4.12-fold in MCF-7 and A549 cells, respectively. The most cytotoxic compounds were subsequently studied for their suppression of kinase activity using the homogeneous time-resolved fluorescence assay versus epidermal growth factor receptor (EGFRWT) and EGFR790M. With IC50 values of 0.28 ± 0.03 and 5.02 ± 0.19, compound 2 was demonstrated to be the most effective against both forms of EGFR. Furthermore, compound 2 also had the best antioxidant property, decreasing the radical scavenging activity by 78%. Molecular docking research, on the other hand, was carried out for the analyzed candidates (1–7) to study their mechanism of action as EGFR inhibitors. In silico absorption, distribution, metabolism, excretion, and toxicity tests were also performed to explain the physicochemical features of the examined derivatives