Abstract
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and location). To fully exploit the pixel redundancy of the background and improve the model’s robustness, we further propose a background perturbation method, which randomly shuffles the background patches without disordering the original nuclei distribution. To achieve contextual consistency between original and template instances, a smooth-GAN is designed with a foreground similarity encoder (FSE) and a triplet loss. We validated the proposed method on two datasets, i.e., Kumar and CPS datasets. Experimental results demonstrate the effectiveness of each component and the superior performance achieved by our method to the state-of-the-art methods.The source code is available at https://github.com/hust-linyi/insmix.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., Heng, P.A.: DCAN: deep contour-aware networks for object instance segmentation from histology images. Med. Image Anal. 36, 135–146 (2017)
Chen, S., Ding, C., Tao, D.: Boundary-assisted region proposal networks for nucleus segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 279–288. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_27
Cui, Y., Zhang, G., Liu, Z., Xiong, Z., Hu, J.: A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med. Biol. Eng. Comput. 57(9), 2027–2043 (2019). https://doi.org/10.1007/s11517-019-02008-8
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Dvornik, N., Mairal, J., Schmid, C.: Modeling visual context is key to augmenting object detection datasets. In: Proceedings of the European Conference on Computer Vision, pp. 364–380 (2018)
Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1301–1310 (2017)
Elmore, J.G., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)
Fang, H.S., Sun, J., Wang, R., Gou, M., Li, Y.L., Lu, C.: InstaBoost: Boosting instance segmentation via probability map guided copy-pasting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 682–691 (2019)
French, G., Laine, S., Aila, T., Mackiewicz, M.: Semi-supervised semantic segmentation needs strong, varied perturbations. In: British Machine Vision Conference (2019)
Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918–2928 (2021)
Graham, S., et al.: HoVer-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)
Gupta, A., Dollar, P., Girshick, R.: LVIS: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2961–2969 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2017)
Kumar, N., et al.: A multi-organ nucleus segmentation challenge. IEEE Trans. Med. Imaging 39(5), 1380–1391 (2019)
Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)
Liao, M., et al.: Automatic segmentation for cell images based on bottleneck detection and ellipse fitting. Neurocomputing 173, 615–622 (2016)
Lin, T.-Y.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lin, Y., et al.: Label propagation for annotation-efficient nuclei segmentation from pathology images. arXiv preprint arXiv:2202.08195 (2022)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Naylor, P., Laé, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448–459 (2018)
Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., Zheng, Y.: Instance-aware self-supervised learning for nuclei segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 341–350. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_33
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5505–5514 (2018)
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)
Zeng, Y., Lin, Z., Lu, H., Patel, V.M.: CR-Fill: Generative image inpainting with auxiliary contextual reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14164–14173 (2021)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018)
Zhou, Y., Chen, H., Lin, H., Heng, P.-A.: Deep semi-supervised knowledge distillation for overlapping cervical cell instance segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 521–531. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_51
Zhou, Y., Chen, H., Xu, J., Dou, Q., Heng, P.-A.: IRNet: Instance relation network for overlapping cervical cell segmentation. In: MICCAI 2019. LNCS, vol. 11764, pp. 640–648. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_71
Zhou, Y., Onder, O.F., Dou, Q., Tsougenis, E., Chen, H., Heng, P.-A.: CIA-Net: Robust nuclei instance segmentation with contour-aware information aggregation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 682–693. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_53
Acknowledgments
This work was supported by Beijing Institute of Collaborative Innovation Funding (No. BICI22EG01) and HKSAR RGC General Research Fund (GRF) #16203319.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, Y., Wang, Z., Cheng, KT., Chen, H. (2022). InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-16434-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16433-0
Online ISBN: 978-3-031-16434-7
eBook Packages: Computer ScienceComputer Science (R0)