shape deformation
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2021 ◽  
pp. 2101288
Author(s):  
Pan Jiang ◽  
Yuxin Zhang ◽  
Xiaoxiao Mu ◽  
Desheng Liu ◽  
Yanhua Liu ◽  
...  

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260630
Author(s):  
Moritz Gross ◽  
Michael Spektor ◽  
Ariel Jaffe ◽  
Ahmet S. Kucukkaya ◽  
Simon Iseke ◽  
...  

Purpose Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages. Methods This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages (“All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages (“Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons. Results 219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107). Conclusion To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.


2021 ◽  
Author(s):  
Michal Baranowski ◽  
Lukasz Balewski ◽  
Adam Lamecki ◽  
Michal Mrozowski

2021 ◽  
pp. 118888
Author(s):  
Xiaojia Guo ◽  
Weijuan Huang ◽  
Jun Tong ◽  
Lingyun Chen ◽  
Xiaowen Shi
Keyword(s):  
3D Shape ◽  

Icarus ◽  
2021 ◽  
Vol 365 ◽  
pp. 114505
Author(s):  
Keisuke Sugiura ◽  
Hiroshi Kobayashi ◽  
Sei-ichiro Watanabe ◽  
Hidenori Genda ◽  
Ryuki Hyodo ◽  
...  

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 966
Author(s):  
Hui-Ying Kuo ◽  
John Ching-Jen Hsiao ◽  
Jing-Jie Chen ◽  
Chi-Hung Lee ◽  
Chun-Chao Chuang ◽  
...  

The aim of this study was to determine the relationship between relative peripheral refraction and retinal shape by 2-D magnetic resonance imaging in high myopes. Thirty-five young adults aged 20 to 30 years participated in this study with 16 high myopes (spherical equivalent < −6.00 D) and 19 emmetropes (+0.50 to −0.50 D). An open field autorefractor was used to measure refractions from the center out to 60° in the horizontal meridian and out to around 20° in the vertical meridian, with a step of 3 degrees. Axial length was measured by using A-scan ultrasonography. In addition, images of axial, sagittal, and tangential sections were obtained using 2-D magnetic resonance imaging. The highly myopic group had a significantly relative peripheral hyperopic refraction and showed a prolate ocular shape compared to the emmetropic group. The highly myopic group had relative peripheral hyperopic refraction and showed a prolate ocular form. Significant differences in the ratios of height/axial (1.01 ± 0.02 vs. 0.94 ± 0.03) and width/axial (0.99 ± 0.17 vs. 0.93 ± 0.04) were found from the MRI images between the emmetropic and the highly myopic eyes (p < 0.001). There was a negative correlation between the retina’s curvature and relative peripheral refraction for both temporal (Pearson r = −0.459; p < 0.01) and nasal (Pearson r = −0.277; p = 0.011) retina. For the highly myopic eyes, the amount of peripheral hyperopic defocus is correlated to its ocular shape deformation. This could be the first study investigating the relationship between peripheral refraction and ocular dimension in high myopes, and it is hoped to provide useful knowledge of how the development of myopia changes human eye shape.


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