• Acta Optica Sinica
  • Vol. 44, Issue 7, 0733001 (2024)
Qi Chen1、2, Zhibao Qin1、2, Xiaoyu Cai1、2, Shijie Li1、2, Zijun Wang1、2, Junsheng Shi1、2、*, and Yonghang Tai1、2、*
Author Affiliations
  • 1School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, Yunnan, China
  • 2Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming 650500, Yunnan, China
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    DOI: 10.3788/AOS231537 Cite this Article Set citation alerts
    Qi Chen, Zhibao Qin, Xiaoyu Cai, Shijie Li, Zijun Wang, Junsheng Shi, Yonghang Tai. Dynamic Three-Dimensional Reconstruction of Soft Tissue in Neural Radiation Field for Robotic Surgery Simulators[J]. Acta Optica Sinica, 2024, 44(7): 0733001 Copy Citation Text show less
    Schematic of optically based reproduction of color information of soft tissues
    Fig. 1. Schematic of optically based reproduction of color information of soft tissues
    Key implementation steps
    Fig. 2. Key implementation steps
    Reconstruct the endoscopic surgery scene into a composite radiation field, and use the SASM to divide the endoscopic surgery scene into three regions: dynamic region, static region, and deformable region
    Fig. 3. Reconstruct the endoscopic surgery scene into a composite radiation field, and use the SASM to divide the endoscopic surgery scene into three regions: dynamic region, static region, and deformable region
    Overview of the general framework
    Fig. 4. Overview of the general framework
    By promoting a more concentrated density distribution along each camera ray in the static component, the recovered background contains more highlights to enhance the realism of the image
    Fig. 5. By promoting a more concentrated density distribution along each camera ray in the static component, the recovered background contains more highlights to enhance the realism of the image
    Experimental setup and robotic surgical simulation system
    Fig. 6. Experimental setup and robotic surgical simulation system
    Visualization results of segmentation in real surgical scenes
    Fig. 7. Visualization results of segmentation in real surgical scenes
    Results of each soft tissue reconstruction in the endoscopic scenario of robotic surgery simulation (qualitative)
    Fig. 8. Results of each soft tissue reconstruction in the endoscopic scenario of robotic surgery simulation (qualitative)
    Line graphs of color difference of each organ. (a) Lung color difference line graph; (b) kidney color difference line graph; (c) liver color difference line graph
    Fig. 9. Line graphs of color difference of each organ. (a) Lung color difference line graph; (b) kidney color difference line graph; (c) liver color difference line graph
    Different color difference representations indicate that the color of the reconstructed organ is close to the true color of biological soft tissue. (a) Photographed original kidney map; (b) 3D scatter plot of positional color difference; (c) contour map of magnification chromatic aberration; (d) 3D reconstructed kidney map; (e) vector chromatic aberration map in color coded map of R channel; (f) vector chromatic aberration map in color coded map of G channel
    Fig. 10. Different color difference representations indicate that the color of the reconstructed organ is close to the true color of biological soft tissue. (a) Photographed original kidney map; (b) 3D scatter plot of positional color difference; (c) contour map of magnification chromatic aberration; (d) 3D reconstructed kidney map; (e) vector chromatic aberration map in color coded map of R channel; (f) vector chromatic aberration map in color coded map of G channel
    Performance of reconstructed biological soft tissue improves over time (represented by increasing number of iterations)
    Fig. 11. Performance of reconstructed biological soft tissue improves over time (represented by increasing number of iterations)
    Influence of hyperparameters (the number of input images) on reconstruction performance. (a) PSNR; (b) SSIM; (c) MSSIM
    Fig. 12. Influence of hyperparameters (the number of input images) on reconstruction performance. (a) PSNR; (b) SSIM; (c) MSSIM
    Influence of hyperparameters (number of hidden layers, regularization weights) in hash grids on reconstruction quality. (a) PSNR; (b) MAE; (c) NRMSE
    Fig. 13. Influence of hyperparameters (number of hidden layers, regularization weights) in hash grids on reconstruction quality. (a) PSNR; (b) MAE; (c) NRMSE
    MethodPSNR of lungSSIM of lungLPIPS of lungPSNR of kidneySSIM of kidneyLPIPS of kidneyPSNR of liverSSIM of liverLPIPS of liver
    NeRF19.90.5530.90520.10.5840.89723.50.5960.879
    InstantNGP23.50.7920.81628.50.6960.87126.00.6010.792
    Ours28.60.8600.79229.40.8880.79428.40.8990.738
    Table 1. Dataset reconstructed by the introduction of supervised loss after each soft tissue structure is trained for 50000 times in each robotic surgery simulation scenario, and the dataset is compared with the ground truth
    Qi Chen, Zhibao Qin, Xiaoyu Cai, Shijie Li, Zijun Wang, Junsheng Shi, Yonghang Tai. Dynamic Three-Dimensional Reconstruction of Soft Tissue in Neural Radiation Field for Robotic Surgery Simulators[J]. Acta Optica Sinica, 2024, 44(7): 0733001
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