Abstract
The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. For semantic segmentation of nuclei, Vision Transformers (VT) have the potentiality to outperform Convolutional Neural Network (CNN) based models due to their ability to model long-range dependencies (i.e., global context). Usually, VT and CNN models are pre-trained with large-scale natural image dataset (i.e., ImageNet) in fully-supervised manner. However, pre-training nuclei segmentation models with ImageNet is not much helpful because of morphological and textural differences between natural image domain and medical image domain. Also, ImageNet-like large-scale annotated histology dataset rarely exists in medical image domain. In this paper, we propose a novel region-level Self-Supervised Learning (SSL) approach and corresponding triplet loss for pre-training semantic nuclei segmentation model with unannotated histology images extracted from Whole Slide Images (WSI). Our proposed region-level SSL is based on the observation that, non-background (i.e., nuclei) patches of an input image are difficult to predict from surrounding neighbor patches, and vice versa. We empirically demonstrate the superiority of our proposed SSL incorporated VT model on two public nuclei segmentation datasets.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atito, S., Awais, M., Kittler, J.: Sit: self-supervised vision transformer. arXiv preprint arXiv:2104.03602 (2021)
Carion, N., et al.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149 (2018)
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. arXiv preprint arXiv:2006.09882 (2020)
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 865–872 (2019)
Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 403–412 (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: Resunet-A: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogram. Rem. Sens. 162, 94–114 (2020)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Han, K., et al.: A survey on visual transformer. arXiv preprint arXiv:2012.12556 (2020)
Haq, M.M., Huang, J.: Adversarial domain adaptation for cell segmentation. In: Medical Imaging with Deep Learning, pp. 277–287. PMLR (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
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)
Liu, Y., Sangineto, E., Bi, W., Sebe, N., Lepri, B., Nadai, M.: Efficient training of visual transformers with small datasets. Adv. Neural Inf. Process. Syst. 34 (2021)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mahmood, F., et al.: Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39(11), 3257–3267 (2019)
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)
Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
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
Sahasrabudhe, M., et al.: Self-supervised nuclei segmentation in histopathological images using attention. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 393–402. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_38
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, X., et al.: TransPath: transformer-based self-supervised learning for histopathological image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 186–195. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_18
Xie, E., Wang, W., Wang, W., Ding, M., Shen, C., Luo, P.: Segmenting transparent objects in the wild. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 696–711. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_41
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
Xu, Y., et al.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 18(1), 1–17 (2017)
Yang, J., et al.: Vision-language pre-training with triple contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15671–15680 (2022)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)
Zhou, Z., Rahman S., Md Mahfuzur, Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Acknowledgments
This work was partially supported by the NSF CAREER grant IIS-1553687 and Cancer Prevention and Research Institute of Texas (CPRIT) award (RP190107).
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
Haq, M.M., Huang, J. (2022). Self-supervised Pre-training for Nuclei 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_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-16434-7_30
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)