ArticleScientific reports2022
A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 68 citations in OpenAlex.
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- Automated error localisation and correction techniques for deep-learning-based segmentation of 3D MRI sequences based on feature-derived-region aggregation.Scientific reports · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- RetinaDetachNet: Automated Deep Learning Quantification of Photoreceptor Cell Death for Neuroprotection Studies in Experimental Retinal Detachment.Translational vision science & technology · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Universal consensus 3D segmentation of cells from 2D segmented stacks.Nature methods · 2025Article
- 3DCellComposer - A versatile pipeline utilizing 2D cell segmentation methods for 3D cell segmentation.Methods (San Diego, Calif.) · 2025Article
- Article
- Digitalized organoids: integrated pipeline for high-speed 3D analysis of organoid structures using multilevel segmentation and cellular topology.Nature methods · 2025Article
- Cell lineage-resolved embryonic morphological map reveals signaling associated with cell fate and size asymmetry.Nature communications · 2025Article
- Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.Scientific reports · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.Scientific reports · 2024Article
- Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells.Heliyon · 2024Article
- Automated segmentation and recognition of C. elegans whole-body cells.Bioinformatics (Oxford, England) · 2024Article
- A multiparametric analysis including single-cell and subcellular feature assessment reveals differential behavior of spheroid cultures on distinct ultra-low attachment plate types.Frontiers in bioengineering and biotechnology · 2024Article
- Cellstitch: 3D cellular anisotropic image segmentation via optimal transport.BMC bioinformatics · 2023Article
- Facilitating cell segmentation with the projection-enhancement network.Physical biology · 2023Article
- In-silico and in-vitro morphometric analysis of intestinal organoids.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.