A convolutional neural network segments yeast microscopy images with high accuracy
Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation.
Enregistré dans:
Auteurs principaux: | Nicola Dietler, Matthias Minder, Vojislav Gligorovski, Augoustina Maria Economou, Denis Alain Henri Lucien Joly, Ahmad Sadeghi, Chun Hei Michael Chan, Mateusz Koziński, Martin Weigert, Anne-Florence Bitbol, Sahand Jamal Rahi |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/1386b197c37e4a83a88513c47ac98ec5 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning
par: Stefano Bianchi, et autres
Publié: (2021) -
Automatic recognition of pulse repetition interval modulation using temporal convolutional network
par: Abolfazl Dadgarnia, et autres
Publié: (2021) -
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
par: Yang Li, et autres
Publié: (2021) -
Humanizing the yeast origin recognition complex
par: Clare S. K. Lee, et autres
Publié: (2021) -
DIAGNOSTIC ACCURACY OF GENEXPERT ASSAY AND COMPARISON WITH SMEAR AFB ON BRONCHIAL WASHINGS IN SPUTUM NEGATIVE SUSPECTED PULMONARY TUBERCULOSIS
par: Mahmood Iqbal Malik, et autres
Publié: (2019)