scholarly journals Micro/Nano-Computed Tomography Technology for Quantitative Dynamic, Multi-scale Imaging of Morphogenesis

Author(s):  
Chelsea L. Gregg ◽  
Andrew K. Recknagel ◽  
Jonathan T. Butcher
2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Perrine Chaurand ◽  
Wei Liu ◽  
Daniel Borschneck ◽  
Clément Levard ◽  
Mélanie Auffan ◽  
...  

2015 ◽  
Vol 461 ◽  
pp. 29-36 ◽  
Author(s):  
T. Lowe ◽  
R.S. Bradley ◽  
S. Yue ◽  
K. Barii ◽  
J. Gelb ◽  
...  

2016 ◽  
Vol 165 ◽  
pp. 149-156 ◽  
Author(s):  
Yihuai Zhang ◽  
Xiaomeng Xu ◽  
Maxim Lebedev ◽  
Mohammad Sarmadivaleh ◽  
Ahmed Barifcani ◽  
...  

2022 ◽  
Vol 319 ◽  
pp. 125953
Author(s):  
Sang-Yeop Chung ◽  
Ji-Su Kim ◽  
Tong-Seok Han ◽  
Dietmar Stephan ◽  
Paul H. Kamm ◽  
...  

Author(s):  
D. J. Bull ◽  
J. A. Smethurst ◽  
I. Sinclair ◽  
F. Pierron ◽  
T. Roose ◽  
...  

Vegetation on railway or highway slopes can improve slope stability through the generation of soil pore water suctions by plant transpiration and mechanical soil reinforcement by the roots. To incorporate the enhanced shearing resistance and stiffness of root-reinforced soils in stability calculations, it is necessary to understand and quantify its effectiveness. This requires integrated and sophisticated experimental and multi-scale modelling approaches to develop an understanding of the processes at different length scales, from individual root–soil interaction through to full soil-profile or slope scale. One of the challenges with multi-scale models is ensuring that they sufficiently closely represent real behaviour. This requires calibration against detailed high-quality and data-rich experiments. This study presents a novel experimental methodology, which combines in situ direct shear loading of a willow root-reinforced soil with X-ray computed tomography to capture the three-dimensional chronology of soil and root deformation within the shear zone. Digital volume correlation (DVC) analysis was applied to the computed tomography dataset to obtain full-field three-dimensional displacement and strain information. This paper demonstrates the feasibility and discusses the challenges associated with DVC experiments on root-reinforced soils.


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