Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology

Gadermayr, Michael; Gupta, L. N.; Appel, Vitus; Boor, Peter; Klinkhammer, Barbara M.; Merhof, Dorit

doi:10.1109/tmi.2019.2899364

Cited by 89 publications

(71 citation statements)

References 25 publications

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“…The differences between previous studies [36][37][38][39][40][41][42][43][44] and our contributions are summarized in the Supplementary Figure S6. 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 on all 4 stains represent a first step for future clinical deployment allowing for the detection, segmentation, and ultimately quantification of several normal histologic primitives in all stains routinely used for diagnostic purposes.…”

Section: Q8mentioning

confidence: 61%

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Jayapandian¹,

Chen²,

Janowczyk³

et al. 2021

Kidney International

131

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Section: Q8mentioning

confidence: 61%

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Jayapandian¹,

Chen²,

Janowczyk³

et al. 2021

Kidney International

131

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“…These training data have to cover the whole range of image characteristics, which can be extremely diverse if several degrees of variation occur. 31 Approaches relying on generative adversarial networks (GANs) 32 exhibit the potential to reduce the requirement of large amounts of manual annotations. This, in turn, reduces the barrier to entry for automated image analysis methods in medical imaging.…”

Section: Challengesmentioning

confidence: 99%

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

2020

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Summary Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine-learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, the so-called generative adversarial networks (GANs) applied to histological image data. Besides improving performance, GANs also enable previously intractable application scenarios in this field. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs, and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.

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“…Cycle-GAN has gained more and more attentions in stain normalization, cross modality transfer, i.e., CT-to-MR [26], MR-to-CT [27], low dose CT denoising [28]. Gadermayr et al [29] develop a fully-unsupervised segmentation approach exploiting Cycle-GAN to convert from the image to the label domain. Shaban et al [30] proposed a StainGAN model based on Cycle-GAN for histopathology color normalization.…”

Section: Image-to-image Translationmentioning

confidence: 99%

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks

Liu

Zhang

Liu

et al. 2021

IEEE Trans. Med. Imaging

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Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&Estained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-theart approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.

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Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology

Cited by 89 publications

References 25 publications

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks

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