Creating Three-Dimensional Polymeric Microstructures by Multi-Beam Interference Lithography

It is attractive to produce true three-dimensional (3D) microstructures both rapidly and economically over a large area with negligible defects for a wide range of applications. Multi-beam interference lithography is one of the promising techniques that can create periodic microstructures in polymers without extensive lithography and etching steps. This review discusses the formation of interference patterns, their dependence on beam parameters, the lithographic process, and the applications to the formation of photonic crystals. Various photoresist systems, including thick films of negative-tone and positive-tone photoresists, liquid resins, organic-inorganic hybrids, and holographic polymer-dispersed liquid crystals, are also reviewed

Fabricating Three-Dimensional Polymeric Photonic Structures by Multi-Beam Interference Lithography

The fabrication of true three-dimensional (3D) microstructures both rapidly and economically over a large area with negligible defects is attractive for a wide range of applications. In particular, multi-beam interference lithography is one of the promising techniques that can mass-produce polymeric 3D photonic crystals defect-free over a large area. This review discusses the relationship between beam geometry and the symmetry of the interference patterns, the lithographic process, and various types of photoresist systems, including thick films of negative-tone and positive-tone photoresists, organic-inorganic hybrids, hydrogels, and holographic polymer-dispersed liquid crystals

Photonic Bandgap Structures of Core-Shell Simple Cubic Crystals from Holographic Lithography

We report the investigation of photonic bandgap properties of a core-shell simple cubic structure (air core with a dielectric shell) using a two-parameter level-set approach. The proposed structure can be obtained by partially backfilling high refractive index materials into a polymeric template fabricated by multi-beam interference lithography. We find that the shell formation in the inverted simple cubic structure increases the complete photonic bandgap width by 10–20% in comparison to that of a completely filled structure. The bandgap between the 5th and 6th bands begins to appear at a refractive index contrast of 2.7. This study suggests the importance to investigate the core-shell formation in three-dimensional photonic crystals through backfilling, which may offer an additional control over their photonic bandgap properties

Spatial-temporal Vehicle Re-identification

Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle across multiple cameras. To overcome the challenge, we propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology based on the adaptive Parzen window method and optimally combines the appearance and spatial-temporal similarities through the fusion network. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy. The experimental results support that utilizing spatial and temporal information for ReID can leverage the accuracy of appearance-based methods and effectively deal with appearance ambiguities.Comment: 10 pages, 6 figure

Fabrication of Photonic Crystals with high refractive index

• Complete photonic bandgap • High contrast of refractive index (RI) • Polymer material with a low RI • Inorganic material with a higher RI, such as silicon, titania. • Fabrication of diamond-like PCs by MBIL, • Fabrication of high RI inorganic PCs via double templating, • Core-shell morphology of replica • Pinch-off problem • Development of combined level-surface to address pinch-off problem • Electrodeposition of titania 3D structure • Electrophoretic deposition of surface charged nanoparticle

Imbalanced loss-integrated deep-learning-based ultrasound image analysis for diagnosis of rotator-cuff tear

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Wide-Field 3D Ultrasound Imaging Platform with a Semi-Automatic 3D Segmentation Algorithm for Quantitative Analysis of Rotator Cuff Tears

Rotator cuff tear (RCT) is a common injury that causes pain and disability in adults. The quantitative diagnosis of the RCT can be crucial in determining a treatment plan or monitoring treatment efficacy. Currently, only a few diagnosis tools, such as magnetic resonance imaging (MRI) and ultrasound imaging (US), are utilized for the diagnosis. Specifically, US exhibited comparable performance with MRI while offering a readily available diagnosis of RCTs at a lower cost. However, three-dimensional(3D) US and analysis of the regions are necessary to enable a better diagnosis of RCTs. Therefore, we developed a wide-field 3D US platform with a semi-automatic 3D image segmentation algorithm for 3D quantitative diagnosis of RCTs. The 3D US platform is built based on a conventional 2D US system and obtains 3D US images via linear scanning. With respect to 3D segmentation algorithm based on active contour model, frequency compounding and anisotropic diffusion methods were applied, and their effects on segmentation were discussed. The platform was used for clinical examination after evaluating the platform via the RCT-mimicking phantoms. As verified by the Dice coefficient(average DC: 0.663, volume DC: 0.723), which was approximately up to 50% higher than that obtained with conventional algorithms, the RCT regions segmented by the developed algorithm significantly matched the ground truth. The results indicated that the wide-field 3D US platform with the 3D segmentation algorithm can constitute a useful tool for improving the accuracy in the diagnosis of RCTs, and can eventually lead to better determination of treatment plans and surgical planning.1

The Volume of Subscapularis Muscle Remains Unaffected by Supraspinatus Tendon Tears: Three-dimensionally Reconstructed Magnetic Resonance Imaging Analysis

Background This study aimed to compare the subscapularis muscle volume between the intact groups (group I) and supraspinatus tendon tear groups (group T) based on the sex and three different age groups. Methods Subjects with a group I and subjects with group T without any other lesions were retrospectively evaluated from among patients who received a magnetic resonance imaging (MRI) scan between January 2011 and December 2013. The MRI scans were studied by a consultant radiologist. The subscapularis muscle volume was compared according to the age and sex; the age groups were categorized as patients in their 40s, 50s, and 60s. The volume of subscapularis muscle was measured by three-dimensional reconstructed images acquired through the axial section of 1.5T MRI. Results No statistically significant differences were observed between subscapularis muscle volume of the group I and group T, except for male patients in their 50s (group I: 100,650 mm3 vs. group T: 106,488 mm3) and 60s (group I: 76,347 mm3 vs. group T: 99,549 mm3) (p<0.05). Males had a larger mean volume of subscapularis muscle than females, and the subscapularis muscle volume decreased in a linear manner with increasing age. Conclusions Decrease in subscapularis muscle volume was observed with increasing age, and the impact of supraspinatus tear on subscapularis muscle volume is age and sex dependent