ArticleBMC bioinformatics2020
Microscopy cell nuclei segmentation with enhanced U-Net.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- MBMSA-UNet: A Multi-Scale Attention-Based Instance Segmentation Model for Moso Bamboo Cells.Plants (Basel, Switzerland) · 2026Article
- Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.Frontiers in bioinformatics · 2026Article
- A semi-automated algorithm for image analysis of respiratory organoids.PLoS computational biology · 2025Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Lightweight Evolving U-Net for Next-Generation Biomedical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- StainAI: quantitative mapping of stained microglia and insights into brain-wide neuroinflammation and therapeutic effects in cardiac arrest.Communications biology · 2025Article
- High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.Analytical chemistry · 2025Article
- 3D-Organoid-SwinNet: High-Content Profiling of 3D Organoids.IEEE journal of biomedical and health informatics · 2025Article
- Quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by myotube and nuclei segmentation using deep learning.Bioinformatics (Oxford, England) · 2024Article
- High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.bioRxiv : the preprint server for biology · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Instance segmentation of cells and nuclei from multi-organ cross-protocol microscopic images.Quantitative imaging in medicine and surgery · 2024Article
- Cell Nuclear Segmentation of B-ALL Images Based on MSFF-SegNeXt.Journal of multidisciplinary healthcare · 2024Article
- Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.Biological imaging · 2024Article
- Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva.Scientific reports · 2023Article
- Single-cell morphological and transcriptome analysis unveil inhibitors of polyploid giant breast cancer cells in vitro.Communications biology · 2023Article
- Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm.Cell reports methods · 2023Article
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Abstract
backgroundCell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing.
resultsAn enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time.
conclusionsOur results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.
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