Literature DB >> 35218549

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

Anthony Bilodeau1,2, Catherine Bouchard1,2, Flavie Lavoie-Cardinal3,4.   

Abstract

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Deep learning; Machine learning; Microscopy; Quantitative analysis; Segmentation

Mesh:

Year:  2022        PMID: 35218549     DOI: 10.1007/978-1-0716-2051-9_20

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  16 in total

1.  An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

Authors:  Yuri Boykov; Vladimir Kolmogorov
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2004-09       Impact factor: 6.226

Review 2.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

3.  Metadata matters: access to image data in the real world.

Authors:  Melissa Linkert; Curtis T Rueden; Chris Allan; Jean-Marie Burel; Will Moore; Andrew Patterson; Brian Loranger; Josh Moore; Carlos Neves; Donald Macdonald; Aleksandra Tarkowska; Caitlin Sticco; Emma Hill; Mike Rossner; Kevin W Eliceiri; Jason R Swedlow
Journal:  J Cell Biol       Date:  2010-05-31       Impact factor: 10.539

4.  U-Net: deep learning for cell counting, detection, and morphometry.

Authors:  Thorsten Falk; Dominic Mai; Robert Bensch; Özgün Çiçek; Ahmed Abdulkadir; Yassine Marrakchi; Anton Böhm; Jan Deubner; Zoe Jäckel; Katharina Seiwald; Alexander Dovzhenko; Olaf Tietz; Cristina Dal Bosco; Sean Walsh; Deniz Saltukoglu; Tuan Leng Tay; Marco Prinz; Klaus Palme; Matias Simons; Ilka Diester; Thomas Brox; Olaf Ronneberger
Journal:  Nat Methods       Date:  2018-12-17       Impact factor: 28.547

5.  Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

Authors:  Ignacio Arganda-Carreras; Verena Kaynig; Curtis Rueden; Kevin W Eliceiri; Johannes Schindelin; Albert Cardona; H Sebastian Seung
Journal:  Bioinformatics       Date:  2017-08-01       Impact factor: 6.937

6.  OMERO: flexible, model-driven data management for experimental biology.

Authors:  Chris Allan; Jean-Marie Burel; Josh Moore; Colin Blackburn; Melissa Linkert; Scott Loynton; Donald Macdonald; William J Moore; Carlos Neves; Andrew Patterson; Michael Porter; Aleksandra Tarkowska; Brian Loranger; Jerome Avondo; Ingvar Lagerstedt; Luca Lianas; Simone Leo; Katherine Hands; Ron T Hay; Ardan Patwardhan; Christoph Best; Gerard J Kleywegt; Gianluigi Zanetti; Jason R Swedlow
Journal:  Nat Methods       Date:  2012-02-28       Impact factor: 28.547

7.  The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets.

Authors:  J R Anderson; S Mohammed; B Grimm; B W Jones; P Koshevoy; T Tasdizen; R Whitaker; R E Marc
Journal:  J Microsc       Date:  2011-01       Impact factor: 1.758

8.  Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons.

Authors:  Flavie Lavoie-Cardinal; Anthony Bilodeau; Mado Lemieux; Marc-André Gardner; Theresa Wiesner; Gabrielle Laramée; Christian Gagné; Paul De Koninck
Journal:  Sci Rep       Date:  2020-07-20       Impact factor: 4.379

9.  Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.

Authors:  Juan C Caicedo; Allen Goodman; Kyle W Karhohs; Beth A Cimini; Jeanelle Ackerman; Marzieh Haghighi; CherKeng Heng; Tim Becker; Minh Doan; Claire McQuin; Mohammad Rohban; Shantanu Singh; Anne E Carpenter
Journal:  Nat Methods       Date:  2019-10-21       Impact factor: 28.547

10.  An annotated fluorescence image dataset for training nuclear segmentation methods.

Authors:  Florian Kromp; Eva Bozsaky; Fikret Rifatbegovic; Lukas Fischer; Magdalena Ambros; Maria Berneder; Tamara Weiss; Daria Lazic; Wolfgang Dörr; Allan Hanbury; Klaus Beiske; Peter F Ambros; Inge M Ambros; Sabine Taschner-Mandl
Journal:  Sci Data       Date:  2020-08-11       Impact factor: 6.444

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