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10.29007/r6cd ◽  
2022 ◽  
Author(s):  
Hoang Nhut Huynh ◽  
My Duyen Nguyen ◽  
Thai Hong Truong ◽  
Quoc Tuan Nguyen Diep ◽  
Anh Tu Tran ◽  
...  

Segmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about the required body part. However, segmenting medical images presents a number of challenges, including the need for medical professionals to be trained, the fact that it is time-consuming and prone to errors. As a result, it appears that an automated medical image segmentation system is required. Deep learning algorithms have recently demonstrated superior performance for segmentation tasks, particularly semantic segmentation networks that provide a pixel-level understanding of images. U- Net for image segmentation is one of the modern complex networks in the field of medical imaging; several segmentation networks have been built on its foundation with the advancements of Recurrent Residual convolutional units and the construction of recurrent residual convolutional neural network based on U-Net (R2U-Net). R2U-Net is used to perform trachea and bronchial segmentation on a dataset of 36,000 images. With a variety of experiments, the proposed segmentation resulted in a dice-coefficient of 0.8394 on the test dataset. Finally, a number of research issues are raised, indicating the need for future improvements.


2022 ◽  
Author(s):  
Pouya Gourani ◽  
Amirhossein Barati Sedeh ◽  
Hajar Zareyi ◽  
Milad Shirvaliloo ◽  
Roghayeh Sheervalilou ◽  
...  

Abstract Background: The present study has attempted to gather all the original and relevant data on the application of gold nanoparticles aimed at the improvement of computed tomography image quality and Hounsfield unit in hepatocellular carcinoma. We performed a systematic review on the studies indexed in PubMed from January 2000 to January 2020. Afterwards, the study design and quality were evaluated. Results: An increase in the nanoparticles concentration and incubation time was associated with improved image quality and the Hounsfield Unit of computed tomography. Conclusion: This study highlights the considerable diagnostic role of gold nanoparticle as novel contrast agents in the early detection of hepatocellular carcinoma.


2021 ◽  
Vol 16 (12) ◽  
pp. P12019
Author(s):  
M. Wang ◽  
M. Zhao ◽  
M. Yao ◽  
J. Liu ◽  
R. Guo

Abstract The accuracy of the existing single slice and Fourier rebinning algorithms depends on the projection angle of the line of response. The increase of such projection angle with the detector size, typical in the large axial space of γ-photon industrial detection, and the loss of some projection data after rebinning, result in the degradation of the image quality. In addition, those algorithms consider the probability of positron annihilation equally distributed along the line of response, which prevents to estimate accurately the positions of the annihilation point, and can originate artifacts and noise in the reconstructed image. In this work, we propose an alternative large axial space rebinning algorithm. In that algorithm, initially the line of response is divided into transverse and axial components. Then, each line of response is uniformly rebinned into all the 2D sinogram data intersecting with it. To improve the accuracy of the estimate of the annihilation point location and suppress the noise effectively, we assign a Gaussian weight coefficient to the projection data, and optimise the rebinning algorithm with it. Finally, we reconstruct the image on the basis of the 2D sinograms with the optimised weights. On the computational side, the algorithm is also accelerated by making use of parallel computing. Both simulation and experimental results show that the proposed method improves the contrast and spatial resolution of 2D reconstructed images. Furthermore, the reconstruction time is not affected by the new method, which is therefore expected to meet the demand of γ-photon industrial inspection imaging.


Author(s):  
Manuel A. Espinoza Rueda ◽  
Marco A. Alcántara Meléndez ◽  
Roberto Muratalla González ◽  
Arnoldo S. Jiménez Valverde ◽  
Juan.F. García García ◽  
...  

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