Abstract
We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at https://github.com/nadeemlab/DeepLIIF.
P. Ghahremani, J. Marino, C. H. Chung, and S. Nadeem—Equal contribution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ciompi, F., Jiao, Y., Laak, J.: Lymphocyte assessment hackathon (LYSTO) (2019). https://zenodo.org/record/3513571
Ghahremani, P., et al.: Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification. Nat. Mach. Intell. 4, 401–412 (2022)
Ghahremani, P., Marino, J., Dodds, R., Nadeem, S.: Deepliif: an online platform for quantification of clinical pathology slides. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21399–21405 (2022)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Kirillov, A., He, K., Girshick, R., Dollár, P.: A unified architecture for instance and semantic segmentation (2017). http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
Koohbanani, N.A., Jahanifar, M., Tajadin, N.Z., Rajpoot, N.: NuClick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020)
Liu, S., Zhu, C., Xu, F., Jia, X., Shi, Z., Jin, M.: BCI: Breast cancer immunohistochemical image generation through pyramid pix2pix (Accepted CVPR Workshop). arXiv preprint arXiv:2204.11425 (2022)
Liu, S., et al.: AdaAttN: revisit attention mechanism in arbitrary neural style transfer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6649–6658 (2021)
Martinez, N., Sapiro, G., Tannenbaum, A., Hollmann, T.J., Nadeem, S.: Impartial: partial annotations for cell instance segmentation. bioRxiv, pp. 2021–01 (2021)
Reisenbichler, E.S., et al.: Prospective multi-institutional evaluation of pathologist assessment of pd-l1 assays for patient selection in triple negative breast cancer. Mod. Pathol. 33(9), 1746–1752 (2020)
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
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100–106 (2021)
Swiderska-Chadaj, Z., et al.: Learning to detect lymphocytes in immunohistochemistry with deep learning. Med. Image Anal. 58, 101547 (2019)
Zhou, Z., Rahman Siddiquee, M.M., 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
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
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ghahremani, P. et al. (2023). An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_68
Download citation
DOI: https://doi.org/10.1007/978-3-031-43987-2_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43986-5
Online ISBN: 978-3-031-43987-2
eBook Packages: Computer ScienceComputer Science (R0)