Deep learning for nuclear image analysis and application to 3D plant nuclei
| Autor: | Mougeot, Guillaume, Chausse, Frédéric, Desset, Sophie, Graumann, Katja |
|---|---|
| Přispěvatelé: | Sciencesconf.org, CCSD, Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Génétique, Reproduction et Développement (GReD), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Oxford Brookes University, Centre National de la Recherche Scientifique [CNRS] |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Confocal microscopy
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 3D segmentation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [INFO]Computer Science [cs] Deep learning 3D plant nucleus [INFO] Computer Science [cs] |
| Zdroj: | ORASIS 2021 ORASIS 2021, Centre National de la Recherche Scientifique [CNRS], Sep 2021, Saint Ferréol, France |
| Popis: | International audience; The continuous development of microscopy has led biologists to have access to large high-resolution 2D and 3D image datasets. Automatic analysis of cellular and nuclear images has become an important challenge in the bioimaging field. To help biologists extract information from these images, tools have been designed to count objects in the image, study object type, their localization or morphology. The current state-of-the-art is led by deep learning methods. Their development relies on the availability of large data sets, on enhancing graphical processing units (GPU) of computers and on developments of new methodologies such as convolutional neural networks (CNN). However, non-IT users may experience difficulties when trying to use these on their own images. This short paper contains our first results after reviewing the state-of-the-art methods in the domain. It first introduces the current difficulties when working with bioimages, then lists the existing datasets for nuclear images analysis. It then exposes some of the easy-to-use tools for bioimaging and points out the different problems related to their use. It finally presents a new dataset for 3D images of plant nuclei that is designed for benchmarking purposes. This results should shortly be published in an journal of biology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|