Lymphocytes subtyping on H&E slides with automatic labelling through same-tissue stained ImmunoFluorescence images

Etienne Pochet, Luis Cano Ayestas, Alhassan Casse, Qi Tang, Roger Trullo
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 254:13-24, 2024.

Abstract

Accurate identification and classification of immune cells within tissue samples are critical for understanding disease mechanisms and predicting treatment responses as a cornerstone for personalized medicine. Traditional histopathology relies on hematoxylin and eosin (H&E) staining, which provides structural context but lacks specificity for immune cell sub-types, preventing pathologists from more precise identification. In contrast, immunofluorescent (IF) staining enables precise targeting of specific markers, but this recently developed technology is very costly and not widely applied in clinical practice yet. In this work, we propose a method to leverage registered pairs of H&E and IF stained images from the same tissue to automatically generate cell type labels for H&E from IF marker expression, allowing for precise identification. In particular, we demonstrate the feasibility of lymphocyte sub-typing from H&E images by training cell-level classifiers to accurately distinguish T-cells subtypes (CD45 / CD3e / CD4 / CD8a). Full code will be made available.

Cite this Paper

BibTeX
@InProceedings{pmlr-v254-pochet24a, title = {Lymphocytes subtyping on H&E slides with automatic labelling through same-tissue stained ImmunoFluorescence images}, author = {Pochet, Etienne and Ayestas, Luis Cano and Casse, Alhassan and Tang, Qi and Trullo, Roger}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {13--24}, year = {2024}, editor = {Ciompi, Francesco and Khalili, Nadieh and Studer, Linda and Poceviciute, Milda and Khan, Amjad and Veta, Mitko and Jiao, Yiping and Haj-Hosseini, Neda and Chen, Hao and Raza, Shan and Minhas, FayyazZlobec, Inti and Burlutskiy, Nikolay and Vilaplana, Veronica and Brattoli, Biagio and Muller, Henning and Atzori, Manfredo and Raza, Shan and Minhas, Fayyaz}, volume = {254}, series = {Proceedings of Machine Learning Research}, month = {06 Oct}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v254/main/assets/pochet24a/pochet24a.pdf}, url = {https://proceedings.mlr.press/v254/pochet24a.html}, abstract = {Accurate identification and classification of immune cells within tissue samples are critical for understanding disease mechanisms and predicting treatment responses as a cornerstone for personalized medicine. Traditional histopathology relies on hematoxylin and eosin (H&E) staining, which provides structural context but lacks specificity for immune cell sub-types, preventing pathologists from more precise identification. In contrast, immunofluorescent (IF) staining enables precise targeting of specific markers, but this recently developed technology is very costly and not widely applied in clinical practice yet. In this work, we propose a method to leverage registered pairs of H&E and IF stained images from the same tissue to automatically generate cell type labels for H&E from IF marker expression, allowing for precise identification. In particular, we demonstrate the feasibility of lymphocyte sub-typing from H&E images by training cell-level classifiers to accurately distinguish T-cells subtypes (CD45 / CD3e / CD4 / CD8a). Full code will be made available.} }
Endnote
%0 Conference Paper %T Lymphocytes subtyping on H&E slides with automatic labelling through same-tissue stained ImmunoFluorescence images %A Etienne Pochet %A Luis Cano Ayestas %A Alhassan Casse %A Qi Tang %A Roger Trullo %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2024 %E Francesco Ciompi %E Nadieh Khalili %E Linda Studer %E Milda Poceviciute %E Amjad Khan %E Mitko Veta %E Yiping Jiao %E Neda Haj-Hosseini %E Hao Chen %E Shan Raza %E Fayyaz MinhasInti Zlobec %E Nikolay Burlutskiy %E Veronica Vilaplana %E Biagio Brattoli %E Henning Muller %E Manfredo Atzori %E Shan Raza %E Fayyaz Minhas %F pmlr-v254-pochet24a %I PMLR %P 13--24 %U https://proceedings.mlr.press/v254/pochet24a.html %V 254 %X Accurate identification and classification of immune cells within tissue samples are critical for understanding disease mechanisms and predicting treatment responses as a cornerstone for personalized medicine. Traditional histopathology relies on hematoxylin and eosin (H&E) staining, which provides structural context but lacks specificity for immune cell sub-types, preventing pathologists from more precise identification. In contrast, immunofluorescent (IF) staining enables precise targeting of specific markers, but this recently developed technology is very costly and not widely applied in clinical practice yet. In this work, we propose a method to leverage registered pairs of H&E and IF stained images from the same tissue to automatically generate cell type labels for H&E from IF marker expression, allowing for precise identification. In particular, we demonstrate the feasibility of lymphocyte sub-typing from H&E images by training cell-level classifiers to accurately distinguish T-cells subtypes (CD45 / CD3e / CD4 / CD8a). Full code will be made available.
APA
Pochet, E., Ayestas, L.C., Casse, A., Tang, Q. & Trullo, R.. (2024). Lymphocytes subtyping on H&E slides with automatic labelling through same-tissue stained ImmunoFluorescence images. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 254:13-24 Available from https://proceedings.mlr.press/v254/pochet24a.html.

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