ArticleScientific reports2019
DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- 3DCellComposer - A versatile pipeline utilizing 2D cell segmentation methods for 3D cell segmentation.Methods (San Diego, Calif.) · 2025Article
- Distributed and networked analysis of volumetric image data for remote collaboration of microscopy image analysis.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model.Small methods · 2025Article
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Article
- Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN.Bioengineering (Basel, Switzerland) · 2024Article
- AnyStar: Domain randomized universal star-convex 3D instance segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 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
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- Volume Exploration Using Multidimensional Bhattacharyya Flow.IEEE transactions on visualization and computer graphics · 2023Article
- Synthesis of large scale 3D microscopic images of 3D cell cultures for training and benchmarking.PloS one · 2023Article
- Schooling of light reflecting fish.PloS one · 2023Article
- A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture.Biophysics reviews · 2022Review
- 3D GAN image synthesis and dataset quality assessment for bacterial biofilm.Bioinformatics (Oxford, England) · 2022Article
- Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.Journal of cardiovascular development and disease · 2022Review
- Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.BMC bioinformatics · 2022Article
- Efficient automatic 3D segmentation of cell nuclei for high-content screening.BMC bioinformatics · 2022Article
- Deep learning -- promises for 3D nuclear imaging: a guide for biologists.Journal of cell science · 2022Review
- Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.Cancer research · 2022Article
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7 authors.
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Abstract
The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.
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