scholarly journals An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix

2022 ◽  
Vol 3 ◽  
Author(s):  
Nicolas Chiaruttini ◽  
Olivier Burri ◽  
Peter Haub ◽  
Romain Guiet ◽  
Jessica Sordet-Dessimoz ◽  
...  
Keyword(s):  
Multimodal Imaging ◽  
Cellular Level ◽  
Reference Image ◽  
Software Components ◽  
Cellular Resolution ◽  
Slide Image ◽  
Resolution Level ◽  
User Friendly ◽  
Automated Registration ◽  
Whole Slide Images

Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multiple times, either through various systems for multimodal imaging, or using the same system but throughout rounds of sample manipulation (e.g. multiple staining sessions). In both cases slight deformations from one image to another are unavoidable, leading to an imperfect superimposition Redundant and thus a loss of accuracy making it difficult to link measurements, in particular at the cellular level. Using pre-existing software components and developing missing ones, we propose a user-friendly workflow which facilitates the nonlinear registration of whole slide images in order to reach sub-cellular resolution level. The set of whole slide images to register and analyze is at first defined as a QuPath project. Fiji is then used to open the QuPath project and perform the registrations. Each registration is automated by using an elastix backend, or semi-automated by using BigWarp in order to interactively correct the results of the automated registration. These transformations can then be retrieved in QuPath to transfer any regions of interest from an image to the corresponding registered images. In addition, the transformations can be applied in QuPath to produce on-the-fly transformed images that can be displayed on top of the reference image. Thus, relevant data can be combined and analyzed throughout all registered slides, facilitating the analysis of correlative results for multiplexed and multimodal imaging.

scholarly journals A User-Friendly Tool For Cloud-Based Whole Slide Image Segmentation, With Examples From Renal Histopathology

2021 ◽  
Author(s):  
Brendon Lutnick ◽  
David Manthey ◽  
Jan Becker ◽  
Brandon Ginley ◽  
Katharina Moos ◽  
...  
Keyword(s):  
Best Practices ◽  
Interstitial Fibrosis ◽  
Histological Structure ◽  
Murine Models ◽  
Tubular Atrophy ◽  
Model Research ◽  
Slide Image ◽  
Vascular Structures ◽  
User Friendly ◽  
Whole Slide Images

Abstract We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain.

scholarly journals Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

2019 ◽  
Author(s):  
Seda Bilaloglu ◽  
Joyce Wu ◽  
Eduardo Fierro ◽  
Raul Delgado Sanchez ◽  
Paolo Santiago Ocampo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
Neoplastic Tissue ◽  
Multiple Tumor ◽  
Slide Image ◽  
Prediction Systems ◽  
Multi Class Classification ◽  
The Many ◽  
Whole Slide Images

AbstractVisual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

Icytomine: A User-Friendly Tool for Integrating Workflows on Whole Slide Images

2019 ◽  
pp. 181-189
Author(s):  
Daniel Felipe Gonzalez Obando ◽  
Diana Mandache ◽  
Jean-Christophe Olivo-Marin ◽  
Vannary Meas-Yedid
Keyword(s):  
User Friendly ◽  
Whole Slide Images

scholarly journals Characterizing virus-induced gene silencing at the cellular level with in situ multimodal imaging

Plant Methods ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Sadie J. Burkhow ◽  
Nicole M. Stephens ◽  
Yu Mei ◽  
Maria Emilia Dueñas ◽  
Daniel J. Freppon ◽  
...  
Keyword(s):  
Gene Silencing ◽  
Multimodal Imaging ◽  
Cellular Level ◽  
Virus Induced Gene Silencing

scholarly journals QuickStitch for seamless stitching of confocal mosaics through high-pass filtering and recursive normalization

2016 ◽  
Author(s):  
Pavel A. Brodskiy ◽  
Paulina M. Eberts ◽  
Cody Narciso ◽  
Jochen Kursawe ◽  
Alexander Fletcher ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Imaging System ◽  
Tissue Level ◽  
Cellular Level ◽  
Specific Information ◽  
Open Source Tool ◽  
The Individual ◽  
User Friendly ◽  
High Pass

ABSTRACTFluorescence micrographs naturally exhibit darkening around their edges (vignetting), which makes seamless stitching challenging. If vignetting is not corrected for, a stitched image will have visible seams where the individual images (tiles) overlap, introducing a systematic error into any quantitative analysis of the image. Although multiple vignetting correction methods exist, there remains no open-source tool that robustly handles large 2D immunofluorescence-based mosaic images. Here, we develop and validate QuickStitch, a tool that applies a recursive normalization algorithm to stitch large-scale immunofluorescence-based mosaics without incurring vignetting seams. We demonstrate how the tool works successfully for tissues of differing size, morphology, and fluorescence intensity. QuickStitch requires no specific information about the imaging system. It is provided as an open-source tool that is both user friendly and extensible, allowing straightforward incorporation into existing image processing pipelines. This enables studies that require accurate segmentation and analysis of high-resolution datasets when parameters of interest include both cellular-level phenomena and larger tissue-level regions of interest.

scholarly journals Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1398
Author(s):  
Pushpanjali Gupta ◽  
Yenlin Huang ◽  
Prasan Kumar Sahoo ◽  
Jeng-Fu You ◽  
Sum-Fu Chiang ◽  
...  
Keyword(s):  
Interobserver Variability ◽  
Tissue Sample ◽  
Treatment Strategies ◽  
Region Of Interest ◽  
Final Diagnosis ◽  
Colon Tissue ◽  
Automated Method ◽  
Slide Image ◽  
Colon Tissue Sample ◽  
Whole Slide Images

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

QuArray: an application for tissue array whole slide image export and signal analysis

2021 ◽  
Author(s):  
Callum Arthurs ◽  
Aamir Ahmed
Keyword(s):  
Graphical User Interface ◽  
Signal Analysis ◽  
Tissue Array ◽  
Integrated Analysis ◽  
Whole Slide Imaging ◽  
Output Data ◽  
Slide Image ◽  
Disease Stratification ◽  
Downstream Analysis ◽  
User Friendly

Abstract Motivation Tissue array (TA) staining, combined with whole slide imaging (WSI) methods facilitate discovery of biomarkers for diagnosis, prognostication and disease stratification. A key impediment in TA WSI analysis is handling missing tissue and artefacts when identifying tissue cores before quantitative, standardized downstream analysis. There is a need for an open access, user friendly, integrated analysis of the WSI generated using TAs in clinical and scientific research laboratories. Results We have developed QuArray (Quantitative Array Application) for image export and signal analysis of TAs using WSI. The application input is a WSI and a corresponding TA configuration file. QuArray identifies and exports core images and analyses chromogen staining in a simple graphical user interface. Output data is saved to file for further analysis including indexed data. Availabilityand implementation Available for download from https://github.com/c-arthurs/QuArray under an MIT licence.

scholarly journals PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

2020 ◽  
Author(s):  
Ming Wu ◽  
Tim Kacprowski ◽  
Dietmar Zehn
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Cellular Level ◽  
Rna Seq ◽  
Analysis Workflow ◽  
Gene Regulatory ◽  
Cell Trajectory ◽  
User Friendly ◽  
Molecular Expression ◽  
Marker Detection

AbstractSummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way.AvailabilitySource code and documentation are available: https://github.com/MingBit/[email protected]

scholarly journals Defining the area of mitoses counting in invasive breast cancer using whole slide image

2021 ◽  
Author(s):  
Asmaa Ibrahim ◽  
Ayat G. Lashen ◽  
Ayaka Katayama ◽  
Raluca Mihai ◽  
Graham Ball ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Practical Method ◽  
Relative Increase ◽  
Significant Rise ◽  
Mitotic Count ◽  
Morphological Criteria ◽  
Slide Image ◽  
Hematoxylin And Eosin ◽  
Mitotic Counting ◽  
Whole Slide Images

AbstractAlthough counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.

scholarly journals StainNet: A Fast and Robust Stain Normalization Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Hongtao Kang ◽  
Die Luo ◽  
Weihua Feng ◽  
Shaoqun Zeng ◽  
Tingwei Quan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Size ◽  
Reference Image ◽  
Source Image ◽  
Target Image ◽  
Color Distribution ◽  
Color Mapping ◽  
Biomedical Image ◽  
Slide Image ◽  
Comparable Performance

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.

Sign in / Sign up

Export Citation Format

Share Document