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2019 | OriginalPaper | Chapter

A Weakly Supervised Method for Instance Segmentation of Biological Cells

Authors : Fidel A. Guerrero-Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Alexandre Cunha

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

We present a weakly supervised deep learning method to perform instance segmentation of cells present in microscopy images. Annotation of biomedical images in the lab can be scarce, incomplete, and inaccurate. This is of concern when supervised learning is used for image analysis as the discriminative power of a learning model might be compromised in these situations. To overcome the curse of poor labeling, our method focuses on three aspects to improve learning: (i) we propose a loss function operating in three classes to facilitate separating adjacent cells and to drive the optimizer to properly classify underrepresented regions; (ii) a contour-aware weight map model is introduced to strengthen contour detection while improving the network generalization capacity; and (iii) we augment data by carefully modulating local intensities on edges shared by adjoining regions and to account for possibly weak signals on these edges. Generated probability maps are segmented using different methods, with the watershed based one generally offering the best solutions, specially in those regions where the prevalence of a single class is not clear. The combination of these contributions allows segmenting individual cells on challenging images. We demonstrate our methods in sparse and crowded cell images, showing improvements in the learning process for a fixed network architecture.
Literature
1.
go back to reference Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proceedings of IEEE CVPR, pp. 5221–5229 (2017) Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proceedings of IEEE CVPR, pp. 5221–5229 (2017)
2.
go back to reference Berman, M., Rannen Triki, A., Blaschko, M.B.: The Lovász-Softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of IEEE CVPR, pp. 4413–4421 (2018) Berman, M., Rannen Triki, A., Blaschko, M.B.: The Lovász-Softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of IEEE CVPR, pp. 4413–4421 (2018)
3.
go back to reference Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of 13th AISTATS, pp. 249–256 (2010) Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of 13th AISTATS, pp. 249–256 (2010)
4.
go back to reference Guerrero-Pena, F.A., Fernandez, P.D.M., Ren, T.I., Yui, M., Rothenberg, E., Cunha, A.: Multiclass weighted loss for instance segmentation of cluttered cells. In: 2018 25th IEEE ICIP, pp. 2451–2455. IEEE (2018) Guerrero-Pena, F.A., Fernandez, P.D.M., Ren, T.I., Yui, M., Rothenberg, E., Cunha, A.: Multiclass weighted loss for instance segmentation of cluttered cells. In: 2018 25th IEEE ICIP, pp. 2451–2455. IEEE (2018)
5.
go back to reference He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of IEEE ICCV, pp. 2961–2969 (2017) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of IEEE ICCV, pp. 2961–2969 (2017)
6.
go back to reference Kervadec, H., et al.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88–99 (2019) CrossRef Kervadec, H., et al.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88–99 (2019) CrossRef
8.
go back to reference Liang, Q., et al.: Weakly-supervised biomedical image segmentation by reiterative learning. IEEE J. Biomed. Health Inf. 23(3), 1205–1214 (2018) CrossRef Liang, Q., et al.: Weakly-supervised biomedical image segmentation by reiterative learning. IEEE J. Biomed. Health Inf. 23(3), 1205–1214 (2018) CrossRef
Metadata
Title
A Weakly Supervised Method for Instance Segmentation of Biological Cells
Authors
Fidel A. Guerrero-Peña
Pedro D. Marrero Fernandez
Tsang Ing Ren
Alexandre Cunha
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_25

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