Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder–decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks. Traditional Unet and Unet-based networks still have a limitation that is not able to fully exploit the output features of the convolutional units in the node. In this study, we proposed a new network model named TMD-Unet, which had three main enhancements in comparison with Unet: (1) modifying the interconnection of the network node, (2) using dilated convolution instead of the standard convolution, and (3) integrating the multi-scale input features on the input side of the model and applying a dense skip connection instead of a regular skip connection. Our experiments were performed on seven datasets, including many different medical image modalities such as colonoscopy, electron microscopy (EM), dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). The segmentation applications implemented in the paper include EM, nuclei, polyp, skin lesion, left atrium, spleen, and liver segmentation. The dice score of our proposed models achieved 96.43% for liver segmentation, 95.51% for spleen segmentation, 92.65% for polyp segmentation, 94.11% for EM segmentation, 92.49% for nuclei segmentation, 91.81% for left atrium segmentation, and 87.27% for skin lesion segmentation. The experimental results showed that the proposed model was superior to the popular models for all seven applications, which demonstrates the high generality of the proposed model.
Purpose The minimally invasive surgery (MIS) has shown advantages when compared to traditional surgery. However, there are two major challenges in the MIS technique: the limited field of view (FOV) and the lack of depth perception provided by the standard monocular endoscope. Therefore, in this study, we proposed a New Endoscope for Panoramic-View with Focus-Area 3D-Vision (3DMISPE) in order to provide surgeons with a broad view field and 3D images in the surgical area for real-time display. Method The proposed system consisted of two endoscopic cameras fixed to each other. Compared to our previous study, the proposed algorithm for the stitching videos was novel. This proposed stitching algorithm was based on the stereo vision synthesis theory. Thus, this new method can support 3D reconstruction and image stitching at the same time. Moreover, our approach employed the same functions on reconstructing 3D surface images by calculating the overlap region's disparity and performing image stitching with the two-view images from both the cameras. Results The experimental results demonstrated that the proposed method can combine two endoscope's FOV into one wider FOV. In addition, the part in the overlap region could also be synthesized for a 3D display to provide more information about depth and distance, with an error of about 1 mm. In the proposed system, the performance could achieve a frame rate of up to 11.3 fps on a single Intel i5-4590 CPU computer and 17.6 fps on a computer with an additional GTX1060 Nvidia GeForce GPU. Furthermore, the proposed stitching method in this study could be made 1.4 times after when compared to that in our previous report. Besides, our method also improved stitched image quality by significantly reducing the alignment errors or "ghosting" when compared to the SURF-based stitching method employed in our previous study. Conclusion The proposed system can provide a more efficient way for the doctors with a broad area of view while still providing a 3D surface image in real-time applications. Our system give promises to improve existing limitations in laparoscopic surgery such as the limited FOV and the lack of depth perception.
Unusual residual time of image sticking under high-voltage electrostatic discharge (ESD) stress on liquid crystal (LC) cells has been observed. It was found that nanoscaled conductive particles doped in LC cells can significantly reduce the residual time of image sticking and the breakdown voltage of the LC cells. This finding can help to protect the doped cells from the attacks of ESD and thus to improve their displaying performance and reliability. In this study, nanoscaled tin-doped indium oxide (ITO) powders were uniformly mixed with high-resistance LC to form a suspension solution. In order to investigate other effects of ITO particles on the LC at high and low voltages, optical and electrical characteristics were compared for the doped cells and those samples without intentional doping. According to the measurement results, it is interesting to find that, except the breakdown characteristic, no other properties in the doped samples were changed with respect to the displaying functions under normal operational voltage.
In this study, a novel ka‐band compact‐size branch‐line coupler was proposed. The coupler was designed and fabricated with WIN Semiconductors 0.15‐μm pseudomorphic high electron‐mobility transistor process. The design method, simulation data, and measurement results of the proposed coupler were also discussed. The coupler occupied less than 45% of the circuit area compared with conventional design at 30 GHz, and did not implement any lumped elements via‐hole grounding, and bonds wire, making it simpler to design, easier to fabricate, and lower cost. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2950–2953, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22941
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.