ReviewJournal of imaging2022
A Review of Watershed Implementations for Segmentation of Volumetric Images.
Review in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 69 citations in OpenAlex.
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- High-throughput marker-free screening of cytoplasmic vacuoles in adherent cells via Fourier ptychographic microscopy.Biomedical optics express · 2025Article
- Unravelling electro-chemo-mechanical processes in graphite/silicon composites for designing nanoporous and microstructured battery electrodes.Nature nanotechnology · 2025Article
- Investigation of the Evolutionary Patterns of Pore Structures and Mechanical Properties During the Hydration Process of Basalt-Fiber-Reinforced Concrete.Materials (Basel, Switzerland) · 2025Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- Densely Populated Cell and Organelles Segmentation with U-Net Ensembles.bioRxiv : the preprint server for biology · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Image-based 3D genomics through chromatin tracing.Nature reviews. Methods primers · 2024Article
- Article
- An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography.BMC biology · 2023Article
- In-silico and in-vitro morphometric analysis of intestinal organoids.PLoS computational biology · 2023Article
- Graphical Image Region Extraction with K-Means Clustering and Watershed.Journal of imaging · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm-watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.
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Registered trials
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.