scholarly journals Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

2021 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

2021 ◽  
Vol 12 ◽  
Author(s):  
John W. Hickey ◽  
Yuqi Tan ◽  
Garry P. Nolan ◽  
Yury Goltsev
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Clustering Algorithm ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Cell Segmentation ◽  
Imaging Data ◽  
Cell Type ◽  
Multiplexed Imaging ◽  
Four Levels

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.

scholarly journals MATISSE: a method for improved single cell segmentation in imaging mass cytometry

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Matthijs J. D. Baars ◽  
Neeraj Sinha ◽  
Mojtaba Amini ◽  
Annelies Pieterman-Bos ◽  
Stephanie van Dam ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Single Cell ◽  
Tissue Sample ◽  
Cellular Heterogeneity ◽  
Cell Segmentation ◽  
Epithelial Tissue ◽  
Imaging Method ◽  
Protein Markers ◽  
Mass Cytometry ◽  
Accurate Identification

Abstract Background Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm2 area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art. Results MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods. Conclusion MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.

Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data

Cell Systems ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1121-1123
Author(s):  
Inna Averbukh ◽  
Noah F. Greenwald ◽  
Candace C. Liu ◽  
Michael Angelo
Keyword(s):  
Single Cell ◽  
Imaging Data ◽  
Multiplexed Imaging

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

L2MXception: An Improved Xception Network for Classification of Peach diseases 

2020 ◽  
Author(s):  
Na Yao ◽  
Fuchuan Ni ◽  
Ziyan Wang ◽  
Jun Luo ◽  
Wing-Kin Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Identification ◽  
Image Data ◽  
Yield Reduction ◽  
Disease Prediction ◽  
Imaging Data ◽  
Regularization Term ◽  
Detection And Identification ◽  
L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

scholarly journals High-resolution myogenic lineage mapping by single-cell mass cytometry

2017 ◽  
Vol 19 (5) ◽  
pp. 558-567 ◽  
Author(s):  
Ermelinda Porpiglia ◽  
Nikolay Samusik ◽  
Andrew Tri Van Ho ◽  
Benjamin D. Cosgrove ◽  
Thach Mai ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Cell Mass ◽  
Mass Cytometry ◽  
Myogenic Lineage ◽  
Lineage Mapping

scholarly journals Solo: doublet identification via semi-supervised deep learning

2019 ◽  
Author(s):  
Nicholas Bernstein ◽  
Nicole Fong ◽  
Irene Lam ◽  
Margaret Roy ◽  
David G. Hendrickson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Single Cells ◽  
Detection Methods ◽  
Learning Approach ◽  
Rna Seq ◽  
Previous Approach ◽  
Cell Technology

AbstractSingle cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these “doublets” violate the fundamental premise of single cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells beyond any previous approach.

scholarly journals A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution

2021 ◽  
Author(s):  
Michele Bortolomeazzi ◽  
Lucia Montorsi ◽  
Damjan Temelkovski ◽  
Mohamed Reda Keddar ◽  
Amelia Acha-Sagredo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Performance ◽  
Spatial Information ◽  
Single Cell Analysis ◽  
Biological Tissues ◽  
Imaging Data ◽  
Cell Identification ◽  
Spatially Resolved ◽  
Multiplexed Imaging

ABSTRACTMultiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at: https://github.com/ciccalab/SIMPLI.

scholarly journals Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mayar Allam ◽  
Thomas Hu ◽  
Shuangyi Cai ◽  
Krishnan Laxminarayanan ◽  
Robert B. Hughley ◽  
...  
Keyword(s):  
Single Cell ◽  
Biological Tissues ◽  
Chronic Tonsillitis ◽  
Tissue Imaging ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Cell Granularity ◽  
Health And Disease

AbstractDeep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.

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