Aluminum nitride (AlN) thin films are widely investigated due to their unique physical properties and applications in energy harvesting devices, ultrasonic transducers, microelectronics, ...high-frequency wide band communications, and power semiconductor devices. This article reviews recent studies of AlN structures, focusing on their fabrication and novel applications. Various fabrication techniques used to synthesize AlN films are discussed, along with their growth mechanisms and crystal structure. The physical properties of AlN films are summarized, including their mechanical and electrical properties (in particular the piezoelectric behavior). Finally, the application of AlN thin films in the fields of energy harvesting and acoustic devices is discussed in detail. Furthermore, this review proposes perspectives for future development of AlN thin films.
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•This work reviews recent studies of AlN structures, focusing on their fabrication, properties and novel applications.•The application of AlN thin films in the fields of energy harvesting and acoustic devices is discussed in detail.•This review proposes perspectives for future development of AlN thin films.
The measurement of bladder volume is crucial for the diagnosis and treatment of urinary system diseases. Ultrasound imaging, with its non-invasive, radiation-free, and repeatable scanning ...capabilities, has become the preferred method for measuring residual urine volume. Nevertheless, it still faces some challenges, including complex imaging methods leading to longer measurement times and lower spatial resolution. Here, we propose a novel three-point localization method that does not require ultrasound imaging to calculate bladder volume. A corresponding triple-element ultrasound probe has been designed based on this method, enabling the ultrasound probe to transmit and receive ultrasound waves in three directions. Furthermore, we utilize the Hilbert Transform algorithm to extract the envelope of the ultrasound signal to enhance the efficiency of bladder volume measurements. The experiment indicates that bladder volume estimation can be completed within 5 s, with a relative error rate of less than 15%. These results demonstrate that this novel three-point localization method offers an effective approach for bladder volume measurement in patients with urological conditions.
We report that efficient high dielectric polymer/ceramic composite materials can be optically printed into three-dimensional (3D) capacitor by the projection based stereolithography (SLA) method. ...Surface decoration of Ag on Pb(Zr,Ti)O3(PZT@Ag) particles were used as filler to enhance the dielectric permittivity. Polymer nanocomposites were fabricated by incorporating PZT@Ag particles into the photocurable polymer solutions, followed by exposure to the digitally controlled optical masks to generate 3D structures. The dielectric permittivity of Flex/PZT@Ag composite reaches as high as 120 at 100Hz with 18vol% filler, which is about 30 times higher than that of pure Flex. Furthermore, the dielectric loss is as low as 0.028 at 100Hz. The results are in good agreement with the effective medium theory (EMT) model. The calculated specific capacitance of our 3D printed capacitor is about 63Fg−1 at the current density of 0.5Ag−1. Cyclic voltammetry (CV) curves indicate 3D printed capacitors possess low resistance and ideal capacitive properties. These results not only provide a tool to fabricate capacitor with complex shapes but lay the groundwork for creating highly efficient polymer-based composites via 3D printing method for electronic applications.
High dielectric polymer/ceramic composite materials can be optically printed into different types of three-dimensional (3D) capacitor (b1–b4) by the projection based stereolithography (SLA) method. Polymer nanocomposites were fabricated by incorporating PZT@Ag particles (Surface decoration of Ag on Pb(Zr,Ti)O3) into photocurable polymer solutions, followed by exposure to digitally controlled optical masks to generate 3D structures (a). Charge–discharge curves (c) indicate 3D printed capacitors possess low resistance and ideal capacitive properties. These results not only provide a tool to fabricate capacitor with complex shapes but lay the groundwork for creating highly efficient polymer-based composites with complicated structures via 3D printing method for electronic applications.
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•We report that high dielectric polymer/ceramic composite materials can be printed into three-dimensional (3D) capacitor using the projection based stereolithography (SLA) method (Fig. 1).•The dielectric permittivity of Flex/PZT@Ag composite reaches as high as 120 at 100Hz with 18vol% filler, which is about 30 times higher than that of pure Flex.•Cyclic voltammetry (CV) curves indicate the 3D printed capacitors have low resistance and ideal capacitive properties (Fig. 5).•The effective permittivity in the PZT composites comes from the incorporation of Ag and the related increase in the average field of both polymer matrix and ceramic filler (Fig. 3).
Accurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing ...because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.
First, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.
In this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors' manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.
S-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.
Lead halide perovskites have exhibited excellent performance in solar cells, LEDs and detectors. Thermal properties of perovskites, such as heat capacity and thermal conductivity, have rarely been ...studied and corresponding devices have barely been explored. Considering the high absorption coefficient (10
~10
cm
), low specific heat capacity (296-326 J kg
K
) and small thermal diffusion coefficient (0.145 mm
s
), herein we showcase the successful use of perovskite in optoacoustic transducers. The theoretically calculated phonon spectrum shows that the overlap of optical phonons and acoustic phonons leads to the up-conversion of acoustic phonons, and thus results in experimentally measured low thermal diffusion coefficient. The assembled device of PDMS/MAPbI
/PDMS simultaneously achieves broad bandwidths (-6 dB bandwidth: 40.8 MHz; central frequency: 29.2 MHz), and high conversion efficiency (2.97 × 10
), while all these parameters are the record values for optoacoustic transducers. We also fabricate miniatured devices by assembling perovskite film onto fibers, and clearly resolve the fine structure of fisheyes, which demonstrates the strong competitiveness of perovskite based optoacoustic transducers for ultrasound imaging.
Ultrasound is extensively studied for biomedical engineering applications. As the core part of the ultrasonic system, the ultrasound transducer plays a significant role. For the purpose of meeting ...the requirement of precision medicine, the main challenge for the development of ultrasound transducer is to further enhance its performance. In this article, an overview of recent developments in ultrasound transducer technologies that use a variety of material strategies and device designs based on both the piezoelectric and photoacoustic mechanisms is provided. Practical applications are also presented, including ultrasound imaging, ultrasound therapy, particle/cell manipulation, drug delivery, and nerve stimulation. Finally, perspectives and opportunities are also highlighted.
Lead telluride (PbTe) is one of the reliable candidates for infrared (IR) optoelectronics with optimum band-gap as well as excellent photoelectric properties. Great interests had been paid on the ...growth and device applications with PbTe for the development of high-performance IR photodetectors especially those working in the near-infrared regime. Although a great deal of effort had been made to prepare PbTe nanostructures for miniaturized detectors, it is difficult to synthesize high-quality two-dimensional (2D) PbTe crystals due to its rock-salt non-layered structure. Herein, a facile strategy for controllable synthesis of ultrathin crystalline PbTe nanosheets by van der Waals epitaxy is reported. With an optimized growth temperature, which determines the morphology transit from triangular pyramid islands to regular square 2D planars, PbTe nanosheets in lateral size of tens of microns with thickness down to ~ 7 nm are achieved. Meanwhile, ultrasensitive near-infrared detectors (NIRDs) based on the as-grown 2D PbTe nanosheets have been demonstrated with an ultrahigh responsivity exceeding 3,847 A/W at the wavelength of 1,550 nm under room temperature. Our approach demonstrates that 2D PbTe nanosheets have great latent capacity of developing high-performance miniaturized IR optoelectronic devices.
Introduction
Radiation therapy is a common treatment option for Head and Neck Cancer (HNC), where the accurate segmentation of Head and Neck (HN) Organs-AtRisks (OARs) is critical for effective ...treatment planning. Manual labeling of HN OARs is time-consuming and subjective. Therefore, deep learning segmentation methods have been widely used. However, it is still a challenging task for HN OARs segmentation due to some small-sized OARs such as optic chiasm and optic nerve.
Methods
To address this challenge, we propose a parallel network architecture called PCG-Net, which incorporates both convolutional neural networks (CNN) and a Gate-Axial-Transformer (GAT) to effectively capture local information and global context. Additionally, we employ a cascade graph module (CGM) to enhance feature fusion through message-passing functions and information aggregation strategies. We conducted extensive experiments to evaluate the effectiveness of PCG-Net and its robustness in three different downstream tasks.
Results
The results show that PCG-Net outperforms other methods, improves the accuracy of HN OARs segmentation, which can potentially improve treatment planning for HNC patients.
Discussion
In summary, the PCG-Net model effectively establishes the dependency between local information and global context and employs CGM to enhance feature fusion for accurate segment HN OARs. The results demonstrate the superiority of PCGNet over other methods, making it a promising approach for HNC treatment planning.
Currently, the incidence of liver cancer is on the rise annually. Precise identification of liver tumors is crucial for clinicians to strategize the treatment and combat liver cancer. Thus far, liver ...tumor contours have been derived through labor-intensive and subjective manual labeling. Computers have gained widespread application in the realm of liver tumor segmentation. Nonetheless, liver tumor segmentation remains a formidable challenge owing to the diverse range of volumes, shapes, and image intensities encountered.
In this article, we introduce an innovative solution called the attention connect network (AC-Net) designed for automated liver tumor segmentation. Building upon the U-shaped network architecture, our approach incorporates 2 critical attention modules: the axial attention module (AAM) and the vision transformer module (VTM), which replace conventional skip-connections to seamlessly integrate spatial features. The AAM facilitates feature fusion by computing axial attention across feature maps, while the VTM operates on the lowest resolution feature maps, employing multihead self-attention, and reshaping the output into a feature map for subsequent concatenation. Furthermore, we employ a specialized loss function tailored to our approach. Our methodology begins with pretraining AC-Net using the LiTS2017 dataset and subsequently fine-tunes it using computed tomography (CT) and magnetic resonance imaging (MRI) data sourced from Hubei Cancer Hospital.
The performance metrics for AC-Net on CT data are as follows: dice similarity coefficient (DSC) of 0.90, Jaccard coefficient (JC) of 0.82, recall of 0.92, average symmetric surface distance (ASSD) of 4.59, Hausdorff distance (HD) of 11.96, and precision of 0.89. For AC-Net on MRI data, the metrics are DSC of 0.80, JC of 0.70, recall of 0.82, ASSD of 7.58, HD of 30.26, and precision of 0.84.
The comparative experiments highlight that AC-Net exhibits exceptional tumor recognition accuracy when tested on the Hubei Cancer Hospital dataset, demonstrating highly competitive performance for practical clinical applications. Furthermore, the ablation experiments provide conclusive evidence of the efficacy of each module proposed in this article. For those interested, the code for this research article can be accessed at the following GitHub repository: https://github.com/killian-zero/py_tumor-segmentation.git.
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