Acta Optica Sinica, Volume. 41, Issue 15, 1511001(2021)

Deep Learning-Based Detection Method for Mitosis in Living Cells

Baosheng Ke1,2,3, Ying Li1,2,3, Zhenbo Ren1,2,3, Jianglei Di1,2,3、*, and Jianlin Zhao1,2,3、**
Author Affiliations
  • 1School of Physical Science and Technology, Northwestern Polytechnical University, Xi′an, Shaanxi 710129, China
  • 2Shaanxi Key Laboratory of Optical Information Technology, Xi′an, Shaanxi 710129, China
  • 3Key Laboratory of Material Physics and Chemistry Under Extraordinary Conditions, Ministry of Education, Xi′an, Shaanxi 710129, China
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    References(27)

    [1] Kaman E J. Smeulders A W M, Verbeek P W, et al. Image processing for mitoses in sections of breast cancer: a feasibility study[J]. Cytometry, 5, 244-249(1984).

    [2] ten Kate T K, Beliën J A, Smeulders A W et al. Method for counting mitoses by image processing in Feulgen stained breast cancer sections[J]. Cytometry, 14, 241-250(1993).

    [3] Li K, Miller E D, Chen M et al. Computer vision tracking of stemness[C]∥, 847-850(2008).

    [4] Liu A, Li K, Kanade T. Mitosis sequence detection using hidden conditional random fields[C]∥, 580-583(2010).

    [7] Chen C, Jia W W, Wang Y. Recognition and detection of mitosis event based on feature of evolution in time domain[J]. Laser & Optoelectronics Progress, 56, 241007(2019).

    [8] Xing F Y, Xie Y P, Yang L. An automatic learning-based framework for robust nucleus segmentation[J]. IEEE Transactions on Medical Imaging, 35, 550-566(2016).

    [9] Ferrari A, Lombardi S, Signoroni A. Bacterial colony counting with convolutional neural networks in digital microbiology imaging[J]. Pattern Recognition, 61, 629-640(2017).

    [10] Öztürk Ş, Akdemir B. A convolutional neural network model for semantic segmentation of mitotic events in microscopy images[J]. Neural Computing and Applications, 31, 3719-3728(2019).

    [11] Jiménez G, Racoceanu D. Deep learning for semantic segmentation vs. classification in computational pathology: application to mitosis analysis in breast cancer grading[J]. Frontiers in Bioengineering and Biotechnology, 7, 145(2019).

    [12] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

    [13] Girshick R. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1440-1448(2015).

    [14] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [15] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2980-2988(2017).

    [16] [16] RedmonJ, DivvalaS, GirshickR, et al.You only look once: unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE Press, 2016: 779- 788.

    [17] [17] RedmonJ, FarhadiA. YOLO9000: better, faster, stronger[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 6517- 6525.

    [18] Redmon J. -04-08)[2020-11-20]. https:∥arxiv., org/abs/1804, 02767(2018).

    [19] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9905, 21-37(2016).

    [20] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

    [21] Wang C Y. Mark Liao H Y, Wu Y H, et al., 1571-1580(2020).

    [22] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [23] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 3-19(2018).

    [24] Zhao B, Wang C P, Fu Q et al. Multi-scale infrared pedestrian detection based on deep attention mechanism[J]. Acta Optica Sinica, 40, 0504001(2020).

    [25] Oktay O, Schlemper J, Folgoc L L et al. -04-11)[2020-11-20]. https:∥arxiv., org/abs/1804, 03999(2018).

    [26] Li Y, Di J L, Ma C J et al. Quantitative phase microscopy for cellular dynamics based on transport of intensity equation[J]. Optics Express, 26, 586-593(2018).

    [28] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

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    Baosheng Ke, Ying Li, Zhenbo Ren, Jianglei Di, Jianlin Zhao. Deep Learning-Based Detection Method for Mitosis in Living Cells[J]. Acta Optica Sinica, 2021, 41(15): 1511001

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    Paper Information

    Category: Imaging Systems

    Received: Dec. 9, 2020

    Accepted: Mar. 5, 2021

    Published Online: Aug. 11, 2021

    The Author Email: Di Jianglei (jiangleidi@nwpu.edu.cn), Zhao Jianlin (jlzhao@nwpu.edu.cn)

    DOI:10.3788/AOS202141.1511001

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