Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

• URL: http://arxiv.org/abs/2112.05240v1
• Date: Wed, 8 Dec 2021 08:56:15 GMT
• Title: Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning
• Authors: Bijie Bai, Hongda Wang, Yuzhu Li, Kevin de Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan B. Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Wenjie Dong, Morgan Angus Darrow, Elham Kamangar, Han Sung Lee, Yair Rivenson, Aydogan Ozcan
• Abstract summary: We describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory.
• Score: 0.5518574122214462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

Related papers

• Label-free evaluation of lung and heart transplant biopsies using virtual staining [3.24061990641619]
  The traditional histochemical staining process is time-consuming, costly, and labor-intensive. We present a panel of virtual staining neural networks for lung and heart transplant biopsies. Virtual staining networks consistently produce high-quality histology images with high color uniformity.
  arXiv  Detail & Related papers  (2024-09-09T00:18:48Z)
• DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels [10.321593505248341]
  We propose a destain-restain framework for converting H&E staining to IHC staining. We further design loss functions specifically for Hematoxylin and Diaminobenzidin (DAB) channels to generate IHC images.
  arXiv  Detail & Related papers  (2024-09-01T07:56:33Z)
• VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains [0.9920087186610302]
  IHC stains are crucial in pathology practice for resolving complex diagnostic questions and guiding patient treatment decisions. Small biopsies often lack sufficient tissue for multiple stains while preserving material for subsequent molecular testing. VIMs is the first model to address this need, leveraging a large vision-language single-step diffusion model for virtual IHC multiplexing.
  arXiv  Detail & Related papers  (2024-07-26T22:23:45Z)
• Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon [1.741980945827445]
  Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading. Pathologists do need differentchemical (IHC) stains to analyze specific structures or cells. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading.
  arXiv  Detail & Related papers  (2023-08-25T05:24:23Z)
• Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review [17.649693088941508]
  We held the breast cancerchemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations.
  arXiv  Detail & Related papers  (2023-05-05T13:56:02Z)
• Digital staining in optical microscopy using deep learning -- a review [47.86254766044832]
  Digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. We provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.
  arXiv  Detail & Related papers  (2023-03-14T15:23:48Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Deep learning-based transformation of the H&E stain into special stains [44.38127957263123]
  We show the utility of supervised learning-based computational stain transformation from H&E to different special stains using tissue sections from kidney needle core biopsies. Results: The quality of the special stains generated by the stain transformation network was statistically equivalent to those generated through standard histochemical staining.
  arXiv  Detail & Related papers  (2020-08-20T10:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.