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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Leveraging deep learning semantic segmentation for imaging coral skeletons.

Alejandra Coronel-Zegarra1, Jamie L Knaub2, Vivian Merk1

  • 1Department of Chemistry and Biochemistry, Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, the United States of America.

Journal of Structural Biology
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning models, specifically Attention U-Net, accurately segment stony coral skeletons from micro-CT scans. This aids in understanding coral growth, species differences, and disease impacts on skeletal structure.

Keywords:
BiomineralConvolutional neural networksDeep learningImage segmentationMorphogenesisX-ray tomography

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Area of Science:

  • Marine Biology
  • Biomineralization
  • Computational Biology

Background:

  • Micro-computed tomography (micro-CT) and semantic segmentation are crucial for analyzing the complex 3D structures of biomineralized tissues like coral skeletons.
  • Understanding skeletal growth, porosity, density, and thickness variations is vital for coral ecology and disease research.
  • Traditional segmentation methods struggle with large 3D datasets, necessitating advanced automated approaches.

Purpose of the Study:

  • To train and evaluate U-Net deep learning models for segmenting pores and skeletons in stony coral micro-CT datasets.
  • To compare the performance of different U-Net architectures (Attention U-Net, U-Net++, U-Net) for accuracy, efficiency, and generalizability.
  • To apply validated segmentation models for quantitative analysis of coral skeletal properties and disease impacts.

Main Methods:

  • Micro-computed tomography (micro-CT) imaging of stony coral skeletons (Montastraea cavernosa, Porites astreoides).
  • Training and application of U-Net based deep learning models for semantic segmentation of skeletal and pore structures.
  • Statistical evaluation of model performance, including accuracy, computational efficiency, and generalizability.
  • Porosity, bulk density, and skeletal thickness analyses using segmented 3D models.

Main Results:

  • Attention U-Net demonstrated superior performance in accuracy, efficiency, and generalizability compared to U-Net++ and standard U-Net.
  • Deep learning segmentation revealed quantitative differences in skeletal structure between M. cavernosa and P. astreoides.
  • Analysis highlighted variations in skeletal properties between healthy and diseased M. cavernosa, linked to stony coral tissue loss disease.
  • Identified limitations in U-Net models, including potential false-positive and false-negative classifications.

Conclusions:

  • Deep learning, particularly Attention U-Net, offers a powerful and streamlined framework for analyzing complex coral skeletal structures from micro-CT data.
  • This approach enhances our understanding of coral skeletogenesis, interspecies variations, and the effects of diseases like stony coral tissue loss disease.
  • The developed framework facilitates efficient, quantitative analysis of biomineralized tissues, paving the way for broader applications in marine science and paleontology.