Stain normalization of histopathology images using generative adversarial networks

Zanjani, Farhad Ghazvinian; Zinger, Svitlana; Bejnordi, Babak Ehteshami; Laak, J.A.W.M. van der

doi:10.1109/isbi.2018.8363641

Cited by 110 publications

(65 citation statements)

References 17 publications

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“…In this paper, we have used the Macenko algorithm (7) for multiple reasons. (1) From a normalization quality perspective, it is one of the best performing algorithms as shown by the in-depth study presented in Zanjani et al (13).…”

Section: Other Stain Normalization Methodsmentioning

confidence: 99%

“…This method estimates the stain vectors of the WSI of interest by using a singular value decomposition (SVD) approach applied to the non-background pixels of the input image. Using the normalized median intensity (NMI) metric, it was shown in Zanjani et al (13), that the quality of this method is one of the highest when compared to other stain normalization methods. In addition, due to the simplicity of the algorithmic steps, the particular method can be efficiently parallelized.…”

Section: Introductionmentioning

confidence: 99%

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A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

et al. 2019

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Stain normalization is an important processing task for computer-aided diagnosis (CAD) systems in modern digital pathology. This task reduces the color and intensity variations present in stained images from different laboratories. Consequently, stain normalization typically increases the prediction accuracy of CAD systems. However, there are computational challenges that this normalization step must overcome, especially for real-time applications: the memory and run-time bottlenecks associated with the processing of images in high resolution, e.g., 40X. Moreover, stain normalization can be sensitive to the quality of the input images, e.g., when they contain stain spots or dirt. In this case, the algorithm may fail to accurately estimate the stain vectors. We present a high-performance system for stain normalization using a state-of-the-art unsupervised method based on stain-vector estimation. Using a highly-optimized normalization engine, our architecture enables high-speed and large-scale processing of high-resolution whole-slide images. This optimized engine integrates an automated thresholding technique to determine the useful pixels and uses a novel pixel-sampling method that significantly reduces the processing time of the normalization algorithm. We demonstrate the performance of our architecture using measurements from images of different sizes and scanner formats that belong to four different datasets. The results show that our optimizations achieve up to 58x speedup compared to a baseline implementation. We also prove the scalability of our system by showing that the processing time scales almost linearly with the amount of tissue pixels present in the image. Furthermore, we show that the output of the normalization algorithm can be adversely affected when the input images include artifacts. To address this issue, we enhance the stain normalization pipeline by introducing a parameter cross-checking technique that automatically detects the distortion of the algorithm's critical parameters. To assess the robustness of the proposed method we employ a machine learning (ML) pipeline that classifies images for detection of prostate cancer. The results show that the enhanced normalization algorithm increases the classification accuracy of the ML pipeline in the presence of poor-quality input images. For an exemplary ML pipeline, our new method increases the accuracy on an unseen dataset from 0.79 to 0.87.

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Section: Other Stain Normalization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

et al. 2019

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“…Further papers [23,30,34,71,75,87,104,108,112,113] highlight specific applications of machine learning to medical segmentation or further (medical) image processing. Ref.…”

Section: Deep Learningmentioning

confidence: 99%

The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

Lobachev

2019

Preprint

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This work presents two methods that facilitate a 3D reconstruction of microscopic blood vessels in the volume slightly larger than 1 mm 3 . The source of the data are histological serial sections, i. e., microscopic images of probes, stained with immunohistochemistry. Odd and even sections have different stainings in our primary data set. Thus, firstly, an approach to register an alternately-stained series is presented. With image filtering and a feature-detection-based registration we obtain a registered stack of 148 serial sections. The series has missing sections, locally damaged sections, artifacts from acquisition. All these hinder correct connectivity of blood vessels. With our second approach we interpolate the missing information while maintaining the connectivity. We achieve this with deformations based on dense optical flow. The presented methodology is applicable to further histological series. A combination of both approaches allows us to reconstruct more than 76 % larger volumes. An important detail was the composition mode of images. Summarizing, we use methods from image processing and computer vision to create large-scale 3D models from immunostained histological serial sections.

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“…A growing collection of studies have used GANs to synthetically stain images of histological tissue sections, which can save institutions time and money (both in reagents and technologists' time) (Bayramoglu et al, 2017;Borhani et al, 2019;De Biase, 2019;Lahiani et al, 2018;Quiros et al, 2019;Rana et al, 2018;Rivenson, Liu, et al, 2019;Rivenson, Wang, et al, 2019;Xu et al, 2019). GAN models have also been used to remove artificial and natural discolorations in images of stained histological tissue sections, removing artifacts that could perturb deep learning analyses (Bentaieb & Hamarneh, 2018;Ghazvinian Zanjani et al, 2018;Pontalba et al, 2019). Other studies have sought to use GANs to generate synthetic training data to increase the generalizability of deep learning histopathology models .…”

Section: Introductionmentioning

confidence: 99%

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

Levy

Jackson

Sriharan

et al. 2020

Preprint

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Evaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis. APPENDIX 1 Mathematical Description of CycleGAN and Pix2Pix Objective FunctionsGANs are tasked with learning a source/target domain Y from latent noise vector Z via mapping G.The loss function for a GAN is specified by:

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Stain normalization of histopathology images using generative adversarial networks

Cited by 110 publications

References 17 publications

A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

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