Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains.
Philippe Johann To BerensGeoffrey SchivreMarius TheuneJackson PeterSalimata Ousmane SallJérôme MuttererFredy BarnecheClara BourbousseJean MolinierPublished in: Epigenomes (2022)
The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated ( iCRAQ ) and one based on deep learning ( Nucl.Eye.D ), and their evaluation using a collection of A. thaliana nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.
Keyphrases
- deep learning
- convolutional neural network
- sensitive detection
- artificial intelligence
- high resolution
- gene expression
- dna damage
- machine learning
- transcription factor
- genome wide
- single molecule
- endothelial cells
- quantum dots
- high throughput
- systematic review
- air pollution
- working memory
- dna methylation
- oxidative stress
- mass spectrometry
- single cell
- plant growth