Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.
New Zealand 15(Received 2 Purpose: Breast cancer is a major public health issue for women, and early detection significantly increases survival rate. Currently, there is increased research interest in elastographic soft-tissue 20 imaging techniques based on the correlation between pathology and mechanical stiffness.Anthropomorphic breast phantoms are critical for in-vitro validation of emerging elastographic technologies. This research develops heterogeneous breast phantoms for use in testing elastographic imaging modalities.Methods: Mechanical property estimation of eight different elastomers is performed to determine 25 storage moduli (E') and damping ratios (ζ) using a dynamic mechanical analyzer (DMA). Dynamic compression testing was carried out isothermally at room temperature over a range of 4-50 Hz.Silicone compositions with physiologically realistic storage modulus were chosen for mimicking skin adipose, cancerous tumors and pectoral muscles and 13 anthropomorphic breast phantoms were constructed for in vitro trials of Digital Image Elasto Tomography (DIET) breast cancer screening 30 system. A simpler fabrication was used to assess the possibility of multiple tumor detection using Magnetic Resonance Elastography (MRE).Results: Silicone materials with ranges of storage moduli (E') from 2 to 570 kPa and damping ratios (ζ) from 0.03 to 0.56 were identified. The resulting phantoms were tested in two different elastographic breast cancer diagnostic modalities. A significant contrast was successfully identified 35 between healthy tissues and cancerous tumors both in Magnetic Resonance Elastography (MRE) and Digital Image Elasto-tomography (DIET). Conclusions:The phantoms presented promise aid to researchers in elastographic imaging modalities for breast cancer detection and provide a foundation for silicone based phantom materials for mimicking soft tissues of other human organs. 40
Digital Image Elasto Tomography (DIET) is a non-invasive breast cancer screening modality that induces mechanical vibrations into a breast and images its surface motion with digital cameras. A new approach in software based diagnosis of this surface motion is presented, focussing on the second natural frequency of the breast. Separate modal analysis is used to estimate the modal parameters using imaging data from silicone phantoms. The second natural frequency proves to be a reliable metric with the potential to clearly distinguish cancerous and healthy tissue as well as providing an approximate location for the tumor. Furthermore, thorough statistical analysis is performed to verify the results.
Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion. A model-based segmentation is used to identify the profile of the breast in each image, and the 3-D surface is reconstructed by fitting a model to the profiles. The surface motion is measured using a modern optical flow implementation customized to the application, then trajectories of points on the 3-D surface are given by fusing the optical flow with the reconstructed surfaces. On data from human trials, the system is shown to exceed the performance of an earlier marker-based system at tracking skin surface motion. We demonstrate that the system can detect a 10 mm tumor in a silicone phantom breast.
Micro-crack formation and resultant bacterial infiltration are major causes of secondary caries formation in dental resin-based composite restorations. Improving dental resin composites’ mechanical and biological properties using highly bendable nanoparticles (NPs) can resolve this issue. This study aims to develop novel Diethylaminoethyl (DEAE)-Dextran silver nanoparticles (AgNPs) and subsequently modify composite resins with these NPs to enhance their mechanical and antibacterial properties. DEAE-Dextran AgNPs were successfully synthesized using a chemical reduction method that was confirmed with the help of ultraviolet-visible (UV-Vis) spectroscopy, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Zeta potential, and energy-dispersive X-ray spectroscopy (EDS). Antibacterial activity of a composite disc with DEAE-Dextran AgNPs was tested against Streptococcus mutans, Enterococcus faecalis, and oral microcosm. The composite discs prepared with DEAE-Dextran AgNPs exhibited excellent antibacterial activity compared with composite resin reinforced by simple AgNPs (p < 0.05). Mechanical properties were significantly enhanced by adding DEAE-Dextran into composite resin (p < 0.05). Moreover, unlike AgNPs, DEAE-Dextran AgNPs were found to be less hemolytic. The results establish strong ground applications for DEAE-Dextran-modified dental composite resins in restorative dental applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.