Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method Published in: 2019 Medical Technologies Congress (TIPTEKNO) DOI: 10.1109/TIPTEKNO.2019.8894978 Publisher: IEEE Conference Location: Izmir, Turkey

1. Cell Segmentation of 2D Phase-Contrast Microscopy
Images with Deep Learning Method
Aydin Ayanzadeh,1 Hüseyin Onur Yağar,1 Özden Yalçın Özuysal,2 Devrim Pesen Okvur,2
Behçet Uğur Töreyin,1 Devrim Ünay,3 Sevgi Önal2
1
Informatics Institute, Ayazaga Campus, Istanbul Technical University, Istanbul, Turkey
2
Department of Molecular Biology and Genetics, Izmir Institute of Technology, Izmir, Turkey
3
Biomedical Engineering, Faculty of Engineering, Izmir University of Economics, Izmir, Turkey
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2. Overview
1 Motivation
2 Dataset
3 Method
4 Experiment Results
5 Conclusion
6 References
7 Question
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3. Motivation
Motivation
1 Annotation preparation is expensive.
2 Insufficient training samples.
3 Phase-contrast microscopy is
challenging.
4 Manual analysis of time-lapse
microscopy is tedious for biologists.
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4. Dataset
Workflow
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5. Dataset
Related Works
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6. Dataset
Dataset
1 600 Frame which 25 of
them are annotate.
2 normalizing images.
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7. Method
Multi-Resolution Architecture
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8. Method
Data Augmentation
1 n=3 Number of
Transformation
2 k=4 Number of
Augmentation
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9. Method
Evaluation Metrics
Evaluation Metrics
1 IoU
2 F-Score
3 Dice Coefficient
IoU(X, Y ) =
|X
T
Y |
|X
S
Y |
(1)
F −Score =
2 · precision · recall
precision + recall
(2)
Dice(X, Y ) =
2|X · Y |
|X| + |Y |
(3)
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10. Experiment Results
Quantitative Results
Table: Quantitative results of cell segmentation
Methods IoU Dice Coeffıcient F-Score
Emperical Gradient Threshold 0.381 0.578 0.547
PHANTAST 0.597 0.651 0.673
U-Net 0.825 0.854 0.837
Proposed Method 0.871 0.899 0.881
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11. Experiment Results
Qualitative Results
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12. Experiment Results
Qualitative Results
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13. Experiment Results
Visualization
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14. Conclusion
Conclusion
1 we proposed Multi-Resolution network with sequential augmentation
which increase the accuracy of the method in compare of base-line
methods.
2 The results show that our proposed approach outperforms the
state-of-the-art algorithms in completeness, robustness.
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15. Conclusion
Future Work
1 Extend the dataset by increasing manual annotations in segmentation
and Tracking.
2 Then onwards we will fortify our analysis by constructing lineage
relationships to provide information about cell behavior.
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16. Conclusion
Data Citation
Ayanzadeh, Aydin; Yağar, Hüseyin Onur; Özuysal, Özden Yalçın; Okvur,
Devrim Pesen; Töreyin, Behçet Uğur; Ünay, Devrim; et al. (2019): Phase
Contrast Microscopy of cells with annotation. figshare. Dataset.
https://doi.org/10.6084/m9.figshare.8965820
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17. Conclusion
References
T. Kanade, et al., “Cell image analysis: Algorithms, system and applications,” in
WACV. IEEE, 2011, pp. 374–381.
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based
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Jaccard, Nicolas, et al. ”Automated method for the rapid and precise estimation of
adherent cell culture characteristics from phase contrast microscopy images.”
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O. Z. Kraus, J. L. Ba, and B. J. Frey, “Classifying and segmenting microscopy
images with deep multiple instance learning,” Bioinformatics, vol. 32, no. 12, pp.
i52–i59, 2016.
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18. References
References
A. Arbelle and T. Riklin Raviv, “Microscopy cell segmentation via adversarial
neural networks,” arXiv preprint arXiv:1709.05860, 2017.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. ”U-net: Convolutional
networks for biomedical image segmentation.” International Conference on Medical
image computing and computer-assisted intervention. Springer, Cham, 2015.
Arbelle, Assaf, and Tammy Riklin Raviv.”Microscopy cell segmentation via
convolutional LSTM networks.” 2019 IEEE 16th International Symposium on
Biomedical Imaging(ISBI 2019).IEEE, 2019.
Tsai, Hsieh-Fu, et al. ”Usiigaci: Instance-aware cell tracking in stain-free phase
contrast microscopy enabled by machine learning.” SoftwareX 9 (2019): 230-237.
Chalfoun, Joe, et al. ”Empirical gradient threshold technique for automated
segmentation across image modalities and cell lines.” Journal of microscopy 260.1
(2015): 86-99.
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19. References
References
Arbelle, Assaf, and Tammy Riklin Raviv.”Microscopy cell segmentation via
convolutional LSTM networks.” 2019 IEEE 16th International Symposium on
Biomedical Imaging(ISBI 2019).IEEE, 2019.
Tsai, Hsieh-Fu, et al. ”Usiigaci: Instance-aware cell tracking in stain-free phase
contrast microscopy enabled by machine learning.” SoftwareX 9 (2019): 230-237.
Chalfoun, Joe, et al. ”Empirical gradient threshold technique for automated
segmentation across image modalities and cell lines.” Journal of microscopy 260.1
(2015): 86-99.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic
segmentation (2014), arXiv:1411.4038 [cs.CV]
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20. References
References
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis.
Nature Methods 9, 676–682 (2012)
Schneider, C. A., Rasband, W.S.Eliceiri, K. W. NIH image to ImageJ: 25 years of
image analysis. Nature Methods 9 671–675 (2012).
A.Paszke, S.Gross, S.Chintala, G.Chanan, E.Yang, Z.DeVito, Z. Lin, A.Desmaison,
L.Antiga, and A.Lerer.Automatic differentiation in pytorch. In NIPS Workshop,
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Acharjya, P. P., et al. ”A new approach of watershed algorithm using distance
transform applied to image segmentation.” International Journal of Innovative
Research in Computer and Communication Engineering 1.2 (2013): 185-189.
Aydin Ayanzadeh,1 Hüseyin Onur Yağar,1 Özden Yalçın Özuysal,2 Devrim Pesen Okvur,2 Behçet Uğur
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21. Question
Thank You
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