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Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation [article]

Florian Kromp, Lukas Fischer, Eva Bozsaky, Inge Ambros, Wolfgang Doerr, Sabine Taschner-Mandl, Peter Ambros, Allan Hanbury
2019 arXiv   pre-print
Results show that three out of four deep learning architectures (U-Net, U-Net with ResNet34 backbone, Mask R-CNN) can segment fluorescent nuclear images on most of the sample preparation types and tissue  ...  This especially accounts for nuclear images of tissue sections and nuclear images across varying tissue preparations.  ...  ACKNOWLEDGMENT This work was carried out within the Austrian Research Promotion Agency (FFG) COIN Networks projects TISQUANT and VISIOMICS and additionally supported by the Austrian Ministry for Transport  ... 
arXiv:1907.12975v1 fatcat:icjcczkbofhgleoclazudvbf6q

Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation

Florian Kromp, Lukas Fischer, Eva Bozsaky, Inge M. Ambros, Wolfgang Dorr, Klaus Beiske, Peter F. Ambros, Allan Hanbury, Sabine Taschner-Mandl
2021 IEEE Transactions on Medical Imaging  
Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity.  ...  Deep Convolutional Neural Networks have been demonstrated to solve nuclear image segmentation tasks across different imaging modalities, but a systematic comparison on complex immunofluorescence images  ...  This led to the development of new deep learning based architectures to segment these challenging nuclear images [18] - [20] . Naylor et al.  ... 
doi:10.1109/tmi.2021.3069558 pmid:33784615 fatcat:65ujfuqczfbn7etu7rh6i5w6nq

Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images [article]

Nazanin Moradinasab, Yash Sharma, Laura S. Shankman, Gary K. Owens, Donald E. Brown
2022 arXiv   pre-print
In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images.  ...  These challenges of manual counting motivate the need for an automated approach to localize and count the cells in images.  ...  Also, this work was provided partially by a grant to the integrated Translational Health Research Institute (iTHRIV) with funding support from National Center for Advancing Translational Sciences (NCATS  ... 
arXiv:2208.00098v1 fatcat:7qqplxvovrbbfku6wmwyqhnyee

Hoechst Is All You Need: Lymphocyte Classification with Deep Learning [article]

Jessica Cooper, In Hwa Um, Ognjen Arandjelović, David J Harrison
2021 arXiv   pre-print
Our model learns previously unknown morphological features associated with expression of these proteins which can be used to accurately differentiate lymphocyte subtypes for use in key prognostic metrics  ...  such as assessment of immune cell infiltration,and thereby predict and improve patient outcomes without the need for costly multiplex immunofluorescence.  ...  One application of deep learning in this field is segmentation -that is, given some input image, learning to produce class labels for each pixel in that image.  ... 
arXiv:2107.04388v2 fatcat:teyueev4sranbowp227hexm6wy

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

Donato Cascio, Vincenzo Taormina, Giuseppe Raso
2019 Applied Sciences  
This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database.  ...  The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest  ...  Deep learning models are trained using large labeled data sets and neural network architectures that learn features directly from data without having to manually extract them.  ... 
doi:10.3390/app9081618 fatcat:ax4nlk4whjgifk26f4acjmizze

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues [article]

Clarence Yapp, Edward Novikov, Won-Dong Jang, Yu-An Chen, Marcelo Cicconet, Zoltan Maliga, Connor A Jacobson, Donglai Wei, Sandro Santagata, Hanspeter Pfister, Peter Karl Sorger
2021 bioRxiv   pre-print
This starts with identification of nuclei, a challenging problem in tissue imaging that has recently benefited from deep learning.  ...  The two approaches cumulatively and substantially improve segmentation with three different deep learning frameworks, yielding a set of high accuracy segmentation models.  ...  ACKNOWLEDGEMENTS We thank Alyce Chen and Madison Tyler for their help with this manuscript.  ... 
doi:10.1101/2021.04.02.438285 fatcat:jtaec5whn5cqdib7s2wm6mmkji

Deep learning-based molecular morphometrics for kidney biopsies [article]

Marina Zimmermann, Martin Klaus, Milagros N. Wong, Ann-Katrin Thebille, Lukas Gernhold, Christoph Kuppe, Maurice Halder, Jennifer Kranz, Nicola Wanner, Fabian Braun, Sonia Wulf, Thorsten Wiech (+7 others)
2020 bioRxiv   pre-print
Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for  ...  image-to-image translation and dual segmentation tasks, achieving human-level accuracy.  ...  Together, these findings highlight the potential for deep learning-based architectures to enable robust and scalable molecular morphometric analysis of human tissues.  ... 
doi:10.1101/2020.08.23.263392 fatcat:wdlf4exoevekbmrrnxyudtpauy

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

Frank Rojas, Sharia Hernandez, Rossana Lazcano, Caddie Laberiano-Fernandez, Edwin Roger Parra
2022 Frontiers in Oncology  
Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest  ...  The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization  ...  We thank the pathology team in the Department of Translational Molecular Pathology for the image analysis. Editorial support was provided by Mr.  ... 
doi:10.3389/fonc.2022.889886 pmid:35832550 pmcid:PMC9271766 fatcat:aukpekydrbbwdngqvsbkwea6lu

In situ classification of cell types in human kidney tissue using 3D nuclear staining [article]

Andre Woloshuk, Suraj Khochare, Aljohara Fahad Almulhim, Andrew McNutt, Dawson Dean, Daria Barwinska, Michael Ferkowicz, Michael T Eadon, Katherine J Kelly, Kenneth W Dunn, Mohammad A Hasan, Tarek M El-Achkar (+1 others)
2020 bioRxiv   pre-print
We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers  ...  In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain.  ...  Deep learning A custom-made 3D deep convolution neural network (CNN) based model was used for the kidney cell classification.  ... 
doi:10.1101/2020.06.24.167726 fatcat:vqjqb3qdsrdylowxvpkfb52inm

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

Edwin Yuan, Magdalena Matusiak, Korsuk Sirinukunwattana, Sushama Varma, Łukasz Kidziński, Robert West
2021 Frontiers in Immunology  
Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling.  ...  Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images.  ...  A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.  ... 
doi:10.3389/fimmu.2021.765923 pmid:34777384 pmcid:PMC8588845 fatcat:5rwpf2rmsrdq5gd5yzmkwcnyfq

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data

Alexander Effland, Erich Kobler, Anne Brandenburg, Teresa Klatzer, Leonie Neuhäuser, Michael Hölzel, Jennifer Landsberg, Thomas Pock, Martin Rumpf
2019 International Journal of Computer Assisted Radiology and Surgery  
generation based on a color covariance analysis of real data.  ...  Methods We develop a machine learning approach using variational networks for joint image denoising and classification of tissue sections for melanoma, which is an established model tumor for immuno-oncology  ...  All animal experiments were approved by the local government authorities (LANUV, NRW, Germany) and performed according to the institutional and national guidelines for the care and use of laboratory animals  ... 
doi:10.1007/s11548-019-01919-z fatcat:ft6ea6stmjdfzk5dhkhipwobca

User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue

Seth Winfree
2022 Frontiers in Physiology  
Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms.  ...  cell segmentation in images from immunofluorescence, immunohistochemistry and histological stains.  ...  CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nat.  ... 
doi:10.3389/fphys.2022.833333 pmid:35360226 pmcid:PMC8960722 fatcat:gnarbx36avdxzgs5yrm6tusyaq

DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Quantification [article]

Parmida Ghahremani, Yanyun Li, Arie Kaufman, Rami Vanguri, Noah Greenwald, Michael Angelo, Travis J Hollmann, Saad Nadeem
2021 bioRxiv   pre-print
By creating a multitask deep learning framework referred to as DeepLIIF, we are presenting a single step solution to nuclear segmentation and quantitative single-cell IHC scoring.  ...  and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10 and TP53.  ...  Using this framework, we generated nuclear segmentation masks for each registered set of images with precise cell boundary delineation.  ... 
doi:10.1101/2021.05.01.442219 fatcat:ugt6rsfre5b7poymdxooq43thq

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning [article]

Noah F. Greenwald, Geneva Miller, Erick Moen, Alex Kong, Adam Kagel, Christine Camacho Fullaway, Brianna J. McIntosh, Ke Leow, Morgan Sarah Schwartz, Thomas Dougherty, Cole Pavelchek, Sunny Cui (+16 others)
2021 bioRxiv   pre-print
We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data.  ...  Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning.  ...  Acknowledgments We thank Long Cai, Katy Borner, Matt Thomson, Steve Quake, and Markus Covert for interesting discussions; Sean Bendall, David Glass, and Erin McCaffrey for feedback on the manuscript; Roshan  ... 
doi:10.1101/2021.03.01.431313 fatcat:xob6ar7uwfh3bpuwowp2lvyp7u

Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks

Hao Fu, Weiming Mi, Boju Pan, Yucheng Guo, Junjie Li, Rongyan Xu, Jie Zheng, Chunli Zou, Tao Zhang, Zhiyong Liang, Junzhong Zou, Hao Zou
2021 Frontiers in Oncology  
This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset.  ...  However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction.  ...  Model Architecture In our study, a novel deep-learning framework was designed to classify pancreatic histopathological images.  ... 
doi:10.3389/fonc.2021.665929 pmid:34249702 pmcid:PMC8267174 fatcat:axm24ygfzngtvmdmgv6wl7iige
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