ArticlePLoS computational biology2020
Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence and practices of immunofluorescent cell image processing: a systematic review.Frontiers in cellular neuroscience · 2023Pooled it
- Teriparatide reduces spinal neuroinflammatory phenotypes in ovariectomized rats revealed by an artificial intelligence-driven glial cell morphometry of bright-field tissue sections.Microscopy (Oxford, England) · 2026Article
- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- A multicellular analysis calcium imaging toolbox for ImageJ.Cell reports methods · 2026Article
- Image-based drug screening combined with molecular profiling identifies signatures and drivers of therapy resistance in pediatric AML.Cell reports. Medicine · 2025Article
- A Survey on AI-Driven Mouse Behavior Analysis Applications and Solutions.Bioengineering (Basel, Switzerland) · 2024Review
- Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.Scientific reports · 2024Article
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
- Review of data processing of functional optical microscopy for neuroscience.Neurophotonics · 2022Review
- Development and Validation of a Model for Laparoscopic Colorectal Surgical Instrument Recognition Using Convolutional Neural Network-Based Instance Segmentation and Videos of Laparoscopic Procedures.JAMA network open · 2022Article
- Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level.Frontiers in oncology · 2022Review
- TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi.Frontiers in plant science · 2022Article
- ASIST: Annotation-free synthetic instance segmentation and tracking by adversarial simulations.Computers in biology and medicine · 2021Article
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6 authors.
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Abstract
Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively.
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