StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation

Ali Alhaj Abdo, Islem Mhiri, Zeeshan Nisar, Barbara Seeliger, Thomas Lampert
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 254:180-191, 2024.

Abstract

Image-to-image translation (I2I) has advanced digital pathology by enabling knowledge transfer across clinical contexts through unsupervised domain adaptation (UDA). Although promising, most I2I frameworks transfer source-labeled data to target unlabeled data directly in a one-off way. However, translating stains from information-poor domains to information-rich ones can lead to a domain shift problem due to the large discrepancy between domains. To address this issue, we propose StairwayToStain (STS), an unsupervised gradual stain translation framework that uses intermediate stains to bridge the gap between the source and target stain. Our method is grounded in three main phases: (i) measuring the domain shift between different stains, (ii) defining a translation path, and (iii) performing the gradual stain translation. Our method demonstrates its efficacy in improving glomeruli segmentation when translating from immunohistochemical (IHC) to histochemical stains, as well as between different IHC stains. Comprehensive experiments on stain translation demonstrate STS’s competitive results compared to its variants and state-of-the-art direct I2I methods in achieving UDA. Moreover, we are able to generate additional stains during the translation process. Our method presents the first framework for gradual domain adaptation in stain translation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v254-abdo24a, title = {StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation}, author = {Abdo, Ali Alhaj and Mhiri, Islem and Nisar, Zeeshan and Seeliger, Barbara and Lampert, Thomas}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {180--191}, 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/abdo24a/abdo24a.pdf}, url = {https://proceedings.mlr.press/v254/abdo24a.html}, abstract = {Image-to-image translation (I2I) has advanced digital pathology by enabling knowledge transfer across clinical contexts through unsupervised domain adaptation (UDA). Although promising, most I2I frameworks transfer source-labeled data to target unlabeled data directly in a one-off way. However, translating stains from information-poor domains to information-rich ones can lead to a domain shift problem due to the large discrepancy between domains. To address this issue, we propose StairwayToStain (STS), an unsupervised gradual stain translation framework that uses intermediate stains to bridge the gap between the source and target stain. Our method is grounded in three main phases: (i) measuring the domain shift between different stains, (ii) defining a translation path, and (iii) performing the gradual stain translation. Our method demonstrates its efficacy in improving glomeruli segmentation when translating from immunohistochemical (IHC) to histochemical stains, as well as between different IHC stains. Comprehensive experiments on stain translation demonstrate STS’s competitive results compared to its variants and state-of-the-art direct I2I methods in achieving UDA. Moreover, we are able to generate additional stains during the translation process. Our method presents the first framework for gradual domain adaptation in stain translation.} }
Endnote
%0 Conference Paper %T StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation %A Ali Alhaj Abdo %A Islem Mhiri %A Zeeshan Nisar %A Barbara Seeliger %A Thomas Lampert %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-abdo24a %I PMLR %P 180--191 %U https://proceedings.mlr.press/v254/abdo24a.html %V 254 %X Image-to-image translation (I2I) has advanced digital pathology by enabling knowledge transfer across clinical contexts through unsupervised domain adaptation (UDA). Although promising, most I2I frameworks transfer source-labeled data to target unlabeled data directly in a one-off way. However, translating stains from information-poor domains to information-rich ones can lead to a domain shift problem due to the large discrepancy between domains. To address this issue, we propose StairwayToStain (STS), an unsupervised gradual stain translation framework that uses intermediate stains to bridge the gap between the source and target stain. Our method is grounded in three main phases: (i) measuring the domain shift between different stains, (ii) defining a translation path, and (iii) performing the gradual stain translation. Our method demonstrates its efficacy in improving glomeruli segmentation when translating from immunohistochemical (IHC) to histochemical stains, as well as between different IHC stains. Comprehensive experiments on stain translation demonstrate STS’s competitive results compared to its variants and state-of-the-art direct I2I methods in achieving UDA. Moreover, we are able to generate additional stains during the translation process. Our method presents the first framework for gradual domain adaptation in stain translation.
APA
Abdo, A.A., Mhiri, I., Nisar, Z., Seeliger, B. & Lampert, T.. (2024). StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 254:180-191 Available from https://proceedings.mlr.press/v254/abdo24a.html.

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