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A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Abstract

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12–48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.

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Fig. 1: Cryosection to FFPE translation workflow.
Fig. 2: AI-FFPE method architecture.
Fig. 3: SAB and SR loss function effect on the model performance.
Fig. 4: Improvement of artefacts in the brain frozen sections.
Fig. 5: Improvement of artefacts in the lung frozen sections.
Fig. 6: AI-FFPE enhancement of tumour-specific diagnostic patterns.

Data availability

The TCGA diagnostic whole-slide data (GBM, LGG, LUAD and LUSC) and the corresponding labels are available from the NIH genomic data commons (https://portal.gdc.cancer.gov). Restrictions apply to the availability of in-house data, which were used with institutional permission for the purposes of this project. All requests for access to in-house data may be addressed to the corresponding authors and will be processed in accordance with institutional guidelines. Data can only be shared for academic research purposes and will require a material-transfer or data-transfer agreement with the receiving institution.

Code availability

All codes were implemented in Python using PyTorch as the primary deep-learning library. The complete pipeline for processing whole-slide images and for training and evaluating the deep-learning models is available at the AI-FFPE repository at https://github.com/DeepMIALab and can be used to reproduce the experiments reported in this paper.

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Acknowledgements

M.T., K.B.O. and G.I.G. are grateful to the Scientific and Technological Research Council of Turkey (TUBITAK) for a 2232 International Outstanding Researcher Fellowship and to TUBITAK Ulakbim for the Turkish National e-Science e-Infrastructure (TRUBA)-cluster and data-storage services. We also thank Ö. Asar and H. Okut for their guidance and assistance in evaluation of the results.

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Contributions

M.T., F.M. and K.B.O. conceived the study and designed the experiments. K.B.O. performed the experimental analysis. B.D., G.I.G., M.Y.L., E.K., D.D., K.B. and T.Y.C. curated the training and test datasets. K.B.O., S.C., K.B., D.D., G.S., M.T. and F.M. analysed the results. K.B.O., S.C., U.P.H., F.Y., D.F.K.W., M.T. and F.M. prepared the manuscript. M.T. and F.M. supervised the research.

Corresponding authors

Correspondence to Faisal Mahmood or Mehmet Turan.

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Nature Biomedical Engineering thanks Geert Litjens and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of all bench-marked methods’ improvement of various artefacts in brain tissue sections.

Comparison of all bench-marked methods’ improvement of various artefacts in brain tissue sections} AI-FFPE are compared to several unsupervised image-to-image translation methods such as CyleGAN, FastCUT, AI-FFPE without spatial attention block integration but with SR-Loss (AI-FFPE w/o SAB), AI-FFPE without SR loss integration but with SAB (AI-FFPE w/o SR loss), AI-FFPE without both SR loss and SAB (AI-FFPE w/o SAB and SR loss).

Extended Data Fig. 2 Comparison of all bench-marked methods’ improvement of various artefacts in lung tissue sections.

Under the constrain of cycle consistency loss, CycleGAN, in most of the cases, does not implement any changes on the input FS images. Also, FastCUT’s contrastive learning is useful to maximize shared content-related features in between input and synthesized image patches, however, lacks an essential quality required for the improvement of lung WSIs and that is to determine the tissue edges, such as boundaries of vessels or airways, beyond which has to remain untouched.

Extended Data Fig. 3 Cross-organ adaptability of AI-FFPE.

a When the brain patches were processed with the lung-trained AI-FFPE model, many improvements that are observed in the same images processed by the brain-trained model were replicated. However, the staining appeared more red/pink. b Similar but more severe colouring problem is present here. Some cells and nuclei became pink and lost their cellular characteristics becoming unrecognizable. The folding artefact was not rectified as efficiently as it is in the brain-model tested version. However, slightly more of empty spaces are filled with extracellular matrix(ECM) in the lung-model tested version, which did not add any significant diagnostic information compared its brain-model tested version. c When the lung patch was tested on some colouring difference and slight differences in ECM patterns in the parenchyma. Both models preserved the empty space in the middle d staining in more red side of the spectrum, the empty areas are more intensively filled probably due to more dense nature of brain tissue which does not harbour empty spaces filled with air.

Extended Data Fig. 4 Exemplars of failure cases.

a In rare cases of severe freezing artefacts, the model cannot reverse the dislocation of the cells/nuclei resulting from expanding ice crystals pushing the tissue away from its original location. The empty clefts where the ice crystals are formed are filled with the ECM, creating a mesh-like appearance. b The models sometimes show sub-optimal performance in correcting severe chatter artefacts because the images in the FFPE target domain also exhibit a relatively high frequency of chatter artefacts. c Although, in the vast majority of cases, corrected images with folding artefacts show clear clues indicating that the original patches contain folding artefacts, rarely, it might take a bit longer for the examiner to recognise the increase in cell densities is actually due to the corrected folding artefacts. d The arrows show examples of unnecessary orange-pink colouring of some cells/areas. However, these colour aberrations are uncommon and do not seem to affect the diagnostically meaningful patterns present in the images.

Extended Data Fig. 5 Improvement of output image quality throughout the network training.

Network output images for the brain and lung tissue section at different stages of the learning process, that is, after 10k, 50k, 100k, 200k, 400k and 600k In brain sections, the visibility of astrocytic glial neoplastic and stromal cell nuclei, as well as the fibrillar structures improve through iterations. Even though the visual enhancement in the lung tissue sample is highly challenging due to alveolar architecture, significant restoration of the connective tissue is observed as the training progress. At the beginning of training, the diagnostically misguiding regions such as artificial presence of bleeding and blurred nuclear boundary were frequently observed in AI-FFPE patches. However, these issues have been resolved at the end of five epoch.

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Ozyoruk, K.B., Can, S., Darbaz, B. et al. A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nat. Biomed. Eng 6, 1407–1419 (2022). https://doi.org/10.1038/s41551-022-00952-9

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