ArticleComputers in biology and medicine2019
Learn to segment single cells with deep distance estimator and deep cell detector.
Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- A Deep Learning Pipeline for Cell Segmentation and Viability Quantification in 3D Constructs From Fluorescence Microscopy Images.Microscopy research and technique · 2026Article
- A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.Analytical chemistry · 2026Article
- EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.Communications biology · 2025Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- LivecellX: A Scalable Deep Learning Framework for Single-Cell Object-Oriented Analysis in Live-Cell Imaging.bioRxiv : the preprint server for biology · 2025Article
- Densely Populated Cell and Organelles Segmentation with U-Net Ensembles.bioRxiv : the preprint server for biology · 2025Article
- ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.Biology methods & protocols · 2025Article
- PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy.Bioinformatics (Oxford, England) · 2024Article
- DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications.Expert systems with applications · 2024Article
- Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy.Nature methods · 2023Article
- A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images.Materials today. Bio · 2023Article
- A deep learning framework for quantitative analysis of actin microridges.NPJ systems biology and applications · 2023Article
- Efficient end-to-end learning for cell segmentation with machine generated weak annotations.Communications biology · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- Topography of pleural epithelial structure enabled by en face isolation and machine learning.Journal of cellular physiology · 2023Article
- Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.Physical biology · 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
- Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.PLoS computational biology · 2022Review
- Cell segmentation for immunofluorescence multiplexed images using two-stage domain adaptation and weakly labeled data for pre-training.Scientific reports · 2022Article
- Delineating the mechanisms and design principles ofComputational and structural biotechnology journal · 2022Review
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Authors and funding
8 authors.
Funding
Abstract
Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, deep convolutional neural networks (CNN) treat segmentation as a pixel-wise classification problem and have become a general and efficient method for image segmentation. However, cell imaging data often possesses characteristics that adversely affect segmentation accuracy: absence of established training datasets, few pixels on cell boundaries, and ubiquitous blurry features. We developed a strategy that combines strengths of CNN and traditional watershed algorithm. First, we trained a CNN to learn Euclidean distance transform (EDT) of the mask corresponding to the input images (deep distance estimator). Next, we trained a faster R-CNN (Region with CNN) to detect individual cells in the EDT image (deep cell detector). Then, the watershed algorithm performed the final segmentation using the outputs of previous two steps. Tests on a library of fluorescence, phase contrast and differential interference contrast (DIC) images showed that both the combined method and various forms of the pixel-wise classification algorithm achieved similar pixel-wise accuracy. However, the combined method achieved significantly higher cell count accuracy than the pixel-wise classification algorithm did, with the latter performing poorly when separating connected cells, especially those connected by blurry boundaries. This difference is most obvious when applied to noisy images of densely packed cells. Furthermore, both deep distance estimator and deep cell detector converge fast and are easy to train.
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