ArticleJournal of structural biology2017
SuRVoS: Super-Region Volume Segmentation workbench.
Article in Journal of structural biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Article
- AnnoMate: Exploring and annotating integrated molecular data through custom interactive visualizations.Patterns (New York, N.Y.) · 2024Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
- Article
- 3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Chaperonin CCT controls extracellular vesicle production and cell metabolism through kinesin dynamics.Journal of extracellular vesicles · 2023Article
- Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data.Journal of molecular biology · 2023Review
- Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.International journal of computer assisted radiology and surgery · 2023Article
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- A Review of Image-Based Simulation Applications in High-Value Manufacturing.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Guided interactive image segmentation using machine learning and color-based image set clustering.Bioinformatics (Oxford, England) · 2022Article
- Deep Learning-Based Segmentation of Post-Mortem Human's Olfactory Bulb Structures in X-ray Phase-Contrast Tomography.Tomography (Ann Arbor, Mich.) · 2022Article
- SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales.Frontiers in cell and developmental biology · 2022Article
- Article
- Single-Cell Chemistry of Photoactivatable Platinum Anticancer Complexes.Journal of the American Chemical Society · 2021Article
- Single Cell Cryo-Soft X-ray Tomography Shows That EachLife (Basel, Switzerland) · 2021Article
- A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.Journal of the Royal Society, Interface · 2021Article
- Sample preparation strategies for efficient correlation of 3D SIM and soft X-ray tomography data at cryogenic temperatures.Nature protocols · 2021Article
- Quantitative imaging of membrane contact sites for sterol transfer between endo-lysosomes and mitochondria in living cells.Scientific reports · 2021Article
- Serial Block-Face Scanning Electron Microscopy (SBF-SEM) of Biological Tissue Samples.Journal of visualized experiments : JoVE · 2021Article
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Authors and funding
12 authors.
Funding
Abstract
Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets.
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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.