ArticleCommunications biology2023
Efficient end-to-end learning for cell segmentation with machine generated weak annotations.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Neural Network-Based System for Rapid Diagnostic Decision Support Using FLIM Data: Segmentation and Classification in Liver, Skin, and Pancreatic Tissues.International journal of molecular sciences · 2026Article
- Development of Human Conjunctival Goblet Cell Segmentation Datasets to Improve Quantitation.Scientific data · 2026Article
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- Microfluidic Interrogation of Chitin-Induced Calcium Oscillations in the MossPlants (Basel, Switzerland) · 2026Article
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- Calibration of Digital Holographic Camera for Bubble Gas Volumetric Flux Measurements.Sensors (Basel, Switzerland) · 2025Article
- Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.Nature communications · 2025Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- Phenomics Demonstrates Cytokines Additive Induction of Epithelial to Mesenchymal Transition.Journal of cellular physiology · 2025Article
- Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells.Heliyon · 2024Article
- Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder.Artificial intelligence in medicine · 2024Article
- CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia.Cell reports methods · 2023Article
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3 authors.
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Abstract
Automated cell segmentation from optical microscopy images is usually the first step in the pipeline of single-cell analysis. Recently, deep-learning based algorithms have shown superior performances for the cell segmentation tasks. However, a disadvantage of deep-learning is the requirement for a large amount of fully annotated training data, which is costly to generate. Weakly-supervised and self-supervised learning is an active research area, but often the model accuracy is inversely correlated with the amount of annotation information provided. Here we focus on a specific subtype of weak annotations, which can be generated programmably from experimental data, thus allowing for more annotation information content without sacrificing the annotation speed. We designed a new model architecture for end-to-end training using such incomplete annotations. We have benchmarked our method on a variety of publicly available datasets, covering both fluorescence and bright-field imaging modality. We additionally tested our method on a microscopy dataset generated by us, using machine-generated annotations. The results demonstrated that our models trained under weak supervision can achieve segmentation accuracy competitive to, and in some cases, surpassing, state-of-the-art models trained under full supervision. Therefore, our method can be a practical alternative to the established full-supervision methods.
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