Staining normalization in histopathology: Method benchmarking using multicenter dataset
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918068975828992 |
|---|---|
| author | Khan, Umair Härkönen, Jouni Friman, Marjukka Latonen, Leena Kuopio, Teijo Ruusuvuori, Pekka |
| author_facet | Khan, Umair Härkönen, Jouni Friman, Marjukka Latonen, Leena Kuopio, Teijo Ruusuvuori, Pekka |
| contents | Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Staining normalization in histopathology: Method benchmarking using multicenter dataset Khan, Umair Härkönen, Jouni Friman, Marjukka Latonen, Leena Kuopio, Teijo Ruusuvuori, Pekka Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs I.2.1; I.4.0 Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data |
| title | Staining normalization in histopathology: Method benchmarking using multicenter dataset |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs I.2.1; I.4.0 |
| url | https://arxiv.org/abs/2506.19106 |