arXiv subjects

Marc Tramier

Publications and source records attributed to Marc Tramier.

3 recordsLinked to original sources

arXiv2026

Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification

Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised software and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved $93 \pm 2\%$ accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and can be readily adapted to new labeling schemes, classification targets, cell lines, or microscopy modalities through transfer learning. SGAN is well suited for integration into automated microscopes, enabling efficient and adaptable image analysis across diverse biological and microscopy applications.

q-bio.QM↗

arXiv2020

Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 $\pm$ 2.4 % accurate classification of a cell took 68.7 $\pm$ 3.5 ms (mean $\pm$ SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimising these algorithms for smart microscopy.

q-bio.QM↗

arXiv2017

Dynein dynamics at the microtubule plus-ends and cortex during division in the C. $\textit{elegans}$ zygote

During asymmetric cell division, dynein generates forces, which position the spindle to reflect polarity and ensure correct daughter cell fates. The transient cortical localization of dynein raises the question of its targeting. We found that it accumulates at the microtubule plus-ends like in budding yeast, indirectly hitch-hiking on $\text{EBP-2}^{\text{EB1}}$ likely via dynactin. Importantly, this mechanism, which modestly accounts for cortical forces, does not transport dynein, which displays the same binding/unbinding dynamics as $\text{EBP-2}^{\text{EB1}}$. At the cortex, dynein tracks can be classified as having either directed or diffusive-like motion. Diffusive-like tracks reveal force-generating dyneins. Their densities are higher on the posterior tip of the embryos, where $\text{GPR-1/2}^{\text{LGN}}$ concentrate, but their durations are symmetric. Since dynein flows to the cortex are non-polarized, we suggest that this posterior enrichment increases dynein binding, thus accounts for the force imbalance reflecting polarity, and supplements the regulation of mitotic progression via the non-polarized detachment rate.

q-bio.SC↗