Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.
Roberto MorelliLuca ClissaRoberto AmiciMatteo CerriTimna HitrecMarco LuppiLorenzo RinaldiFabio SquarcioAntonio ZoccoliPublished in: Scientific reports (2021)
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to fatigue errors and suffers from arbitrariness due to the operator's interpretation of the borderline cases. We propose a Deep Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we introduce a Unet-like architecture, cell ResUnet (c-ResUnet), and compare its performance against 3 similar architectures. In addition, we evaluate through ablation studies the impact of two design choices, (i) artifacts oversampling and (ii) weight maps that penalize the errors on cells boundaries increasingly with overcrowding. In summary, the c-ResUnet outperforms the competitors with respect to both detection and counting metrics (respectively, [Formula: see text] score = 0.81 and MAE = 3.09). Also, the introduction of weight maps contribute to enhance performances, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior qualitative assessment by domain experts corroborates previous results, suggesting human-level performance inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. Finally, we release the pre-trained model and the annotated dataset to foster research in this and related fields.
Keyphrases
- induced apoptosis
- deep learning
- cell cycle arrest
- high resolution
- single cell
- endothelial cells
- oxidative stress
- optical coherence tomography
- high throughput
- label free
- artificial intelligence
- magnetic resonance
- living cells
- patient safety
- depressive symptoms
- machine learning
- mesenchymal stem cells
- emergency department
- mass spectrometry
- magnetic resonance imaging
- body composition
- cell proliferation
- atrial fibrillation
- peripheral blood
- body weight
- neural network
- cone beam