Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

Wodziński, Marek; Skalski, Andrzej

doi:10.1088/1361-6560/abcad7

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Specifically, we adopted a multi-step, automatic, and non-rigid histological image registration method 62 , 63 and applied it to our dataset. First, the images were converted into grayscale, downsampled, and histogram equalized.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

et al. 2023

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Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

et al. 2023

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“…There is a significant amount of work done with handcrafted features for an alignment task - [4,5,6,7,8,9,10,11] to list a few. Whereas, there were very few CNN based studies on predicting the transformation parameters for a highly deformed pair of images.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Feature based Cross-slide Registration

Awan¹,

Raza²,

Lotz³

et al. 2022

Preprint

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Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. Slides stained with different biomarkers are analysed side by side which may reveal unknown relations between the different biomarkers. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each section to a reference section of the same tissue block an important pre-requisite task before any cross-slide analysis. We propose a deep feature based registration (DFBR) method which utilises data-driven features to estimate the rigid transformation. We adopted a multi-stage strategy for improving the quality of registration. We also developed a visualisation tool to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate transformed source WSI in a pyramidal form. We compared the performance of data-driven features with that of hand-crafted features on the COMET dataset. Our approach can align the images with low registration errors. Generally, the success of non-rigid registration is dependent on the quality of rigid registration. To evaluate the efficacy of the DFBR method, the first two steps of the ANHIR winner's framework are replaced with our DFBR to register challenge provided image pairs. The modified framework produce comparable results to that of challenge winning team.

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“…13 This class of optimization-based methods is widely used in medical imaging 14,15 and has also been applied to problems in pathology. 10,[16][17][18][19] These energy-minimizing methods make explicit model assumptions through the choice of distance measure and regularization scheme. When applying a method to a new dataset, model refinements can be made by adjusting the model's parameters.…”

Section: Introductionmentioning

confidence: 99%

Comparison of consecutive and restained sections for image registration in histopathology

Lotz,

Weiss,

van der Laak

et al. 2023

J. Med. Imag.

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Although the registration of restained sections allows nucleus-level alignment that enables a direct analysis of interacting biomarkers, consecutive sections only allow the transfer of region-level annotations. The latter can be achieved at low computational cost using coarser image resolutions.Purpose: In digital histopathology, virtual multistaining is important for diagnosis and biomarker research. Additionally, it provides accurate ground truth for various deep-learning tasks. Virtual multistaining can be obtained using different stains for consecutive sections or by restaining the same section. Both approaches require image registration to compensate for tissue deformations, but little attention has been devoted to comparing their accuracy.Approach: We compared affine and deformable variational image registration of consecutive and restained sections and analyzed the effect of the image resolution that influences accuracy and required computational resources. The registration was applied to the automatic nonrigid histological image registration (ANHIR) challenge data (230 consecutive slide pairs) and the hyperparameters were determined. Then without changing the parameters, the registration was applied to a newly published hybrid dataset of restained and consecutive sections (HyReCo, 86 slide pairs, 5404 landmarks).Results: We obtain a median landmark error after registration of 6.5 μm (HyReCo) and 24.1 μm (ANHIR) between consecutive sections. Between restained sections, the median registration error is 2.2 and 0.9 μm in the two subsets of the HyReCo dataset. We observe that deformable registration leads to lower landmark errors than affine registration in both cases (p < 0.001), though the effect is smaller in restained sections. Conclusion:Deformable registration of consecutive and restained sections is a valuable tool for the joint analysis of different stains.

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Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

Cited by 11 publications

References 27 publications

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Deep Feature based Cross-slide Registration

Comparison of consecutive and restained sections for image registration in histopathology

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