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Related Experiment Video

Updated: Jun 13, 2025

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography
02:42

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

Published on: January 17, 2025

234

Computational Methods for Image Analysis in Craniofacial Development and Disease.

E James1, A J Caetano1, P T Sharpe2

  • 1Centre for Oral Immunobiology and Regenerative Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

Journal of Dental Research
|September 13, 2024
PubMed
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Advances in bioimage analysis, powered by deep learning and high-throughput imaging, enable dynamic, quantitative, and predictive observations of biological systems, aiding in understanding development and disease.

Area of Science:

  • Biological sciences
  • Biomedical imaging
  • Computational biology

Background:

  • Observation is central to biological sciences, necessitating advancements in imaging technologies.
  • High-throughput sequencing and imaging enable simultaneous spatial and temporal analysis of molecular variations in tissues.
  • Large datasets have driven the evolution of deep learning models for biomedical imaging analysis.

Purpose of the Study:

  • To review recent advances in bioimage analysis techniques.
  • To discuss computational tools for 3D spatial genomics.
  • To highlight applications in craniofacial development and oral disease.

Main Methods:

  • Image restoration, segmentation, and tracking are key bioimage analysis tasks.
  • Deep learning models are utilized for interrogating large biomedical imaging datasets.
Keywords:
advanced imagingbioinformaticsdeep learningdevelopmental biologymultiomicsspatial genomics

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  • Computational tools for creating 3D spatial genomics maps are discussed.

    • Main Results:

    • Combined advances allow for dynamic, quantitative, and predictive observations of organisms and tissues.
    • New computational tools facilitate the creation of 3D spatial genomics maps.
    • Applications demonstrate the utility of these advances in specific biological studies.

    Conclusions:

    • Bioimage analysis, enhanced by deep learning and advanced imaging, is transforming biological insights.
    • These technologies provide powerful tools for understanding complex biological systems and diseases.
    • The discussed methods and applications show significant potential in diagnostic medicine and developmental biology.