Abstract
Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of each cell is annotated. However, it is very time-consuming for preparing such detailed annotation for many cell culture conditions. In this paper, we propose a weakly supervised method that can segment individual cell regions who touch each other with unclear boundaries in dense conditions without the training data for cell regions. We demonstrated the efficacy of our method using several data-set including multiple cell types captured by several types of microscopy. Our method achieved the highest accuracy compared with several conventional methods. In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data. Code is publicly available in https://github.com/naivete5656/WSISPDR.
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Notes
- 1.
In the test, the stained image is not required.
- 2.
In general, Dice takes a small value when the size of the object is small since the small discrepancy can affect to the metric. Since the size of a cell is much smaller than a general object, and thus it takes smaller value than that of a general object.
- 3.
Zhou’s method requires the training images that do not contain any cell. However, we could not make enough training data except (1) C2C12 due to the dense cells.
References
Bensch, R., Ronneberger, O.: Cell segmentation and tracking in phase contrast images using graph cut with asymmetric boundary costs. In: ISBI (2015)
Chalfoun, J., Majurski, M., Dima, A., et al.: Fogbank: a single cell segmentation across multiple cell lines and image modalities. BMC Bioinform. 15(1), 431 (2014)
Ker, D.F.E., Eom, S., Sanami, S., et al.: Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. In: Scientific data (2018)
Li, Q., Arnab, A., Torr, P.H.S.: Weakly- and semi-supervised panoptic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 106–124. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_7
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618–626 (2017)
Yin, Z., Kanade, T., Chen, M.: Understanding the phase contrast optics to restore artifact-free microscopy images for segmentation. Med. Image Anal. 16, 1047–1062 (2012)
Zhou, Y., Zhu, Y., Ye, Q., et al.: Weakly supervised instance segmentation using class peak response. In: CVPR (2018)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number JP18H05104 and JP19K22895.
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Nishimura, K., Ker, D.F.E., Bise, R. (2019). Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_72
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DOI: https://doi.org/10.1007/978-3-030-32239-7_72
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