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Found 5 papers, 1 papers with code
Self-Supervised Learning in Electron Microscopy: Towards a Foundation Model for Advanced Image Analysis
In this work, we explore the potential of self-supervised learning from unlabeled electron microscopy datasets, taking a step toward building a foundation model in this field.
Instance Segmentation of Dislocations in TEM Images
In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties.
Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy
Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates.
Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems
To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation.
A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
Still, the proposed method performs on par with ML-based segmentation approaches usually used in this context.
