DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
- URL: http://arxiv.org/abs/2409.00649v1
- Date: Sun, 1 Sep 2024 07:56:33 GMT
- Title: DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
- Authors: Linda Wei, Shengyi Hua, Shaoting Zhang, Xiaofan Zhang,
- Abstract summary: 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.
- Score: 10.321593505248341
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Breast cancer is a highly fatal disease among cancers in women, and early detection is crucial for treatment. HER2 status, a valuable diagnostic marker based on Immunohistochemistry (IHC) staining, is instrumental in determining breast cancer status. The high cost of IHC staining and the ubiquity of Hematoxylin and Eosin (H&E) staining make the conversion from H&E to IHC staining essential. In this article, we propose a destain-restain framework for converting H&E staining to IHC staining, leveraging the characteristic that H&E staining and IHC staining of the same tissue sections share the Hematoxylin channel. We further design loss functions specifically for Hematoxylin and Diaminobenzidin (DAB) channels to generate IHC images exploiting insights from separated staining channels. Beyond the benchmark metrics on BCI contest, we have developed semantic information metrics for the HER2 level. The experimental results demonstrated that our method outperforms previous open-sourced methods in terms of image intrinsic property and semantic information.
Related papers
- 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) - Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain
Translation with Inconsistent Groundtruth Image Pairs [5.841841666625825]
We present a new loss function, Adaptive Supervised PatchNCE (ASP), to deal with the input to target inconsistencies in a proposed H&E-to-IHC image-to-image translation framework.
In our experiment, we demonstrate that our proposed method outperforms existing image-to-image translation methods for stain translation to multiple IHC stains.
arXiv Detail & Related papers (2023-03-10T19:56:34Z) - 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) - CoRe: An Automated Pipeline for The Prediction of Liver Resection
Complexity from Preoperative CT Scans [53.561797148529664]
Tumors located in critical positions are known to complexify liver resections.
CoRe is an automated medical image processing pipeline for the prediction of postoperative LR complexity.
arXiv Detail & Related papers (2022-10-15T15:29:24Z) - 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) - Label-free virtual HER2 immunohistochemical staining of breast tissue
using deep learning [0.5518574122214462]
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.
arXiv Detail & Related papers (2021-12-08T08:56:15Z) - Edge-competing Pathological Liver Vessel Segmentation with Limited
Labels [61.38846803229023]
There is no algorithm as yet tailored for the MVI detection from pathological images.
This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of vessels, MVI, and carcinoma grades.
We propose an Edge-competing Vessel Network (EVS-Net) which contains a segmentation network and two edge segmentation discriminators.
arXiv Detail & Related papers (2021-08-01T07:28:32Z) - 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.