Automated segmentation of rheumatoid arthritis immunohistochemistry
stained synovial tissue
- URL: http://arxiv.org/abs/2309.07255v1
- Date: Wed, 13 Sep 2023 18:43:14 GMT
- Title: Automated segmentation of rheumatoid arthritis immunohistochemistry
stained synovial tissue
- Authors: Amaya Gallagher-Syed, Abbas Khan, Felice Rivellese, Costantino
Pitzalis, Myles J. Lewis, Gregory Slabaugh, Michael R. Barnes
- Abstract summary: Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue.
It is a highly heterogeneous disease, with wide cellular and molecular variability observed in synovial tissues.
We train a UNET on a hand-curated, real-world multi-centre clinical dataset R4RA, which contains multiple types of IHC staining.
The model obtains a DICE score of 0.865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily
affects the joint's synovial tissue. It is a highly heterogeneous disease, with
wide cellular and molecular variability observed in synovial tissues. Over the
last two decades, the methods available for their study have advanced
considerably. In particular, Immunohistochemistry stains are well suited to
highlighting the functional organisation of samples. Yet, analysis of
IHC-stained synovial tissue samples is still overwhelmingly done manually and
semi-quantitatively by expert pathologists. This is because in addition to the
fragmented nature of IHC stained synovial tissue, there exist wide variations
in intensity and colour, strong clinical centre batch effect, as well as the
presence of many undesirable artefacts present in gigapixel Whole Slide Images
(WSIs), such as water droplets, pen annotation, folded tissue, blurriness, etc.
There is therefore a strong need for a robust, repeatable automated tissue
segmentation algorithm which can cope with this variability and provide support
to imaging pipelines. We train a UNET on a hand-curated, heterogeneous
real-world multi-centre clinical dataset R4RA, which contains multiple types of
IHC staining. The model obtains a DICE score of 0.865 and successfully segments
different types of IHC staining, as well as dealing with variance in colours,
intensity and common WSIs artefacts from the different clinical centres. It can
be used as the first step in an automated image analysis pipeline for synovial
tissue samples stained with IHC, increasing speed, reproducibility and
robustness.
Related papers
- Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology [0.11728348229595655]
We present an autonomous quality and hallucination assessment method (termed AQuA) for virtual tissue staining.
AQuA achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images.
arXiv Detail & Related papers (2024-04-29T06:32:28Z) - 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) - Virtual histological staining of unlabeled autopsy tissue [1.9351365037275405]
We show that a trained neural network can transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin stained versions of the same samples.
Our virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death.
arXiv Detail & Related papers (2023-08-02T03:31:22Z) - 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) - Virtual stain transfer in histology via cascaded deep neural networks [2.309018557701645]
We demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN)
Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E.
We successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain.
arXiv Detail & Related papers (2022-07-14T00:43:18Z) - Ensuring accurate stain reproduction in deep generative networks for
virtual immunohistochemistry [0.0]
Generative Adrial Networks have become exceedingly advanced at mapping one image type another.
CycleGANs can invented tissue structures in pathology image mapping but have a related disposition to generate areas of inaccurate staining.
We describe a modification to mitigate the loss function of a CycleGAN to improve its mapping ability for pathology images.
arXiv Detail & Related papers (2022-04-14T09:51:04Z) - 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) - Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19.
segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues.
To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
arXiv Detail & Related papers (2020-04-22T07:30:56Z) - Alleviating the Incompatibility between Cross Entropy Loss and Episode
Training for Few-shot Skin Disease Classification [76.89093364969253]
We propose to apply Few-Shot Learning to skin disease identification to address the extreme scarcity of training sample problem.
Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to Cross Entropy (CE) under episode training.
We further strengthen the proposed QR loss with a novel adaptive hard margin strategy.
arXiv Detail & Related papers (2020-04-21T00:57:11Z)
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.