• Acta Optica Sinica
  • Vol. 39, Issue 6, 0615006 (2019)
Yu Feng, Benshun Yi*, Chenyue Wu, and Yungang Zhang
Author Affiliations
  • Electronic Information School, Wuhan University, Wuhan, Hubei 430072, China
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    DOI: 10.3788/AOS201939.0615006 Cite this Article Set citation alerts
    Yu Feng, Benshun Yi, Chenyue Wu, Yungang Zhang. Pulmonary Nodule Recognition Based on Three-Dimensional Convolution Neural Network[J]. Acta Optica Sinica, 2019, 39(6): 0615006 Copy Citation Text show less
    References

    [1] Sluimer I, Schilham A, Prokop M et al. Computer analysis of computed tomography scans of the lung: a survey[J]. IEEE Transactions on Medical Imaging, 25, 385-405(2006). http://ieeexplore.ieee.org/document/1610745/

         Sluimer I, Schilham A, Prokop M et al. Computer analysis of computed tomography scans of the lung: a survey[J]. IEEE Transactions on Medical Imaging, 25, 385-405(2006). http://ieeexplore.ieee.org/document/1610745/

    [2] Wang C M. Key techniques for lung nodule detection and classification based on chest imaging[D]. Shenzhen: University of Chinese Academy of Sciences, 1-3(2018).

         Wang C M. Key techniques for lung nodule detection and classification based on chest imaging[D]. Shenzhen: University of Chinese Academy of Sciences, 1-3(2018).

    [3] Messay T, Hardie R C, Rogers S K. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery[J]. Medical Image Analysis, 14, 390-406(2010). http://www.sciencedirect.com/science/article/pii/S1361841510000198

         Messay T, Hardie R C, Rogers S K. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery[J]. Medical Image Analysis, 14, 390-406(2010). http://www.sciencedirect.com/science/article/pii/S1361841510000198

    [4] Setio A A A, Jacobs C, Gelderblom J et al. . Automatic detection of large pulmonary solid nodules in thoracic CT images[J]. Medical Physics, 42, 5642-5653(2015). http://europepmc.org/abstract/MED/26429238

         Setio A A A, Jacobs C, Gelderblom J et al. . Automatic detection of large pulmonary solid nodules in thoracic CT images[J]. Medical Physics, 42, 5642-5653(2015). http://europepmc.org/abstract/MED/26429238

    [5] Setio A A A, Ciompi F, Litjens G et al. . Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks[J]. IEEE Transactions on Medical Imaging, 35, 1160-1169(2016). http://ieeexplore.ieee.org/document/7422783/

         Setio A A A, Ciompi F, Litjens G et al. . Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks[J]. IEEE Transactions on Medical Imaging, 35, 1160-1169(2016). http://ieeexplore.ieee.org/document/7422783/

    [6] Dou Q, Chen H, Yu L Q et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection[J]. IEEE Transactions on Biomedical Engineering, 64, 1558-1567(2017). http://www.ncbi.nlm.nih.gov/pubmed/28113302

         Dou Q, Chen H, Yu L Q et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection[J]. IEEE Transactions on Biomedical Engineering, 64, 1558-1567(2017). http://www.ncbi.nlm.nih.gov/pubmed/28113302

    [7] Jin H S, Li Z Y, Tong R F et al. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection[J]. Medical Physics, 45, 2097-2107(2018). http://onlinelibrary.wiley.com/doi/10.1002/mp.12846/pdf

         Jin H S, Li Z Y, Tong R F et al. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection[J]. Medical Physics, 45, 2097-2107(2018). http://onlinelibrary.wiley.com/doi/10.1002/mp.12846/pdf

    [8] Dou Q, Chen H, Jin Y M et al. Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning[M]. ∥Descoteaux M, Maier-Hein L, Franz A, et al. Springer Proceedings in Physics. Singapore: Springer, 630-638(2017).

         Dou Q, Chen H, Jin Y M et al. Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning[M]. ∥Descoteaux M, Maier-Hein L, Franz A, et al. Springer Proceedings in Physics. Singapore: Springer, 630-638(2017).

    [9] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

         Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

    [10] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 7132-7141(2018).

         Hu J, Shen L, Sun G. Squeeze-and-excitation networks. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 7132-7141(2018).

    [11] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2999-3007(2017).

         Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2999-3007(2017).

    [12] Li Z Q, Zhu R F, Gao F et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 38, 0828001(2018).

         Li Z Q, Zhu R F, Gao F et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 38, 0828001(2018).

    [13] Lü X Q, Wu L, Gu Y et al. Detection of low dose CT pulmonary nodules based on 3D convolution neural network[J]. Optics and Precision Engineering, 26, 1211-1218(2018).

         Lü X Q, Wu L, Gu Y et al. Detection of low dose CT pulmonary nodules based on 3D convolution neural network[J]. Optics and Precision Engineering, 26, 1211-1218(2018).

    [14] Miao G, Li C F. Detection of pulmonary nodules CT images combined with two-dimensional and three-dimensional convolution neural networks[J]. Laser & Optoelectronics Progress, 55, 051006(2018).

         Miao G, Li C F. Detection of pulmonary nodules CT images combined with two-dimensional and three-dimensional convolution neural networks[J]. Laser & Optoelectronics Progress, 55, 051006(2018).

    [15] Setio A A A, Traverso A, de Bel T et al. . Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge[J]. Medical Image Analysis, 42, 1-13(2017). http://europepmc.org/abstract/MED/28732268

         Setio A A A, Traverso A, de Bel T et al. . Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge[J]. Medical Image Analysis, 42, 1-13(2017). http://europepmc.org/abstract/MED/28732268

    [16] Messay T, Hardie R C, Tuinstra T R. Segmentation of pulmonary nodules in computed tomography using aregression neural network approach and its application to the lung image database consortium and image database resource initiative dataset[J]. Medical Image Analysis, 22, 48-62(2015). http://www.sciencedirect.com/science/article/pii/S1361841515000316

         Messay T, Hardie R C, Tuinstra T R. Segmentation of pulmonary nodules in computed tomography using aregression neural network approach and its application to the lung image database consortium and image database resource initiative dataset[J]. Medical Image Analysis, 22, 48-62(2015). http://www.sciencedirect.com/science/article/pii/S1361841515000316

    [17] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1026-1034(2015).

         He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1026-1034(2015).

    [18] Sakamoto M, Nakano H, Zhao K et al. Multi-stage neural networks with single-sided classifiers for false positive reduction and its evaluation using lung X-Ray CT images[M]. ∥Battiato S, Gallo G, Schettini R, et al. Image Analysis and Processing-ICIAP 2017. Cham: Springer, 370-379(2017).

         Sakamoto M, Nakano H, Zhao K et al. Multi-stage neural networks with single-sided classifiers for false positive reduction and its evaluation using lung X-Ray CT images[M]. ∥Battiato S, Gallo G, Schettini R, et al. Image Analysis and Processing-ICIAP 2017. Cham: Springer, 370-379(2017).

    Yu Feng, Benshun Yi, Chenyue Wu, Yungang Zhang. Pulmonary Nodule Recognition Based on Three-Dimensional Convolution Neural Network[J]. Acta Optica Sinica, 2019, 39(6): 0615006
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