ReviewPLoS computational biology2022
Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.
Review in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.PLoS computational biology · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.bioRxiv : the preprint server for biology · 2026Article
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- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- A high-throughput differential chemical genetic screen uncovers genotype-specific compounds altering plant growth.iScience · 2025Article
- CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.GigaScience · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications.Expert systems with applications · 2024Article
- AI models for automated segmentation of engineered polycystic kidney tubules.Scientific reports · 2024Article
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning.Plant methods · 2024Article
- Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.IEEE transactions on medical imaging · 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
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Authors and funding
6 authors.
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Abstract
Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data.
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