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Classification of Bones

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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Summary

This summary is machine-generated.

Deep learning image analysis helps classify bone health from Synchrotron Radiation micro-Computed Tomography (SR-microCT) scans. This method improves accuracy in distinguishing healthy, osteoporotic, and COVID-19 affected femoral heads.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Biomedical Imaging
Artificial Intelligence in Medicine
Bone Biology

Background:

Human visual inspection of Synchrotron Radiation micro-Computed Tomography (SR-microCT) images struggles to identify subtle bone microscale differences.
Deep Learning (DL) offers potential for analyzing complex imaging data but often requires guidance to focus on relevant details.

Purpose of the Study:

To develop and evaluate a DL-based method for classifying femoral head images from SR-microCT scans.
To differentiate between healthy, osteoporotic, and COVID-19 affected bone tissue using microscale features.

Main Methods:

Utilized unsupervised patch-based clustering to inform a vgg16 model.
Focused on subtle microscale differences in SR-microCT images of femoral heads.
Applied the method to classify images into healthy, osteoporotic, and COVID-19 categories.

Main Results:

Achieved up to 9.8% accuracy improvement in classifying healthy versus osteoporotic images compared to uninformed methods.
Demonstrated 59.1% accuracy in distinguishing between osteoporosis and COVID-19 affected bone.
Established a classification accuracy of 60.91% for healthy versus osteoporotic bone.

Conclusions:

The proposed DL method effectively classifies SR-microCT images of femoral heads based on subtle, human-imperceptible microscale differences.
This approach provides a foundation for automated diagnosis of bone conditions like osteoporosis and COVID-19 related bone changes using advanced imaging techniques.