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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.
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The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

Gross Anatomy of Bone

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The two main features of a long bone are the diaphysis and the epiphysis.
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Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.

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Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept.

Alina Littek¹, Stephen J McKenna¹, Wei Xiong Chiam¹

¹Computer Vision and Image Processing Group, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK.

|April 28, 2023

Summary

This study introduces a deep learning approach for automatically analyzing human cortical bone microstructure. The computer vision model accurately differentiates intact and fragmentary osteons, potentially speeding up anthropological assessments.

Keywords:

cortical bone microstructure deep learning identification osteons semantic segmentation

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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:

Forensic Anthropology
Biological Anthropology
Computer Vision

Background:

Cortical bone microstructure analysis is vital for age-at-death estimation and animal-human differentiation in anthropology.
Osteon frequency and metrics are key features, but current assessment is manual, time-consuming, and requires specialized training.

Purpose of the Study:

To investigate the feasibility of using deep learning for the automatic analysis of human bone microstructure images.
To develop a computer vision model capable of semantic segmentation of osteonal structures.

Main Methods:

A U-Net deep learning architecture was employed for semantic segmentation of bone microstructure images.
Images were classified into three categories: intact osteons, fragmentary osteons, and background.
Data augmentation techniques were utilized to prevent model overfitting.

Main Results:

The model achieved a Dice coefficient of 0.73 for intact osteons, 0.38 for fragmented osteons, and 0.81 for background.
The average Dice coefficient across all classes was 0.64.
A binary classification of osteon-background yielded a Dice coefficient of 0.82.

Conclusions:

This study presents the first proof of concept for using deep learning and computer vision to differentiate intact and fragmentary osteons in human cortical bone.
The automated approach shows potential to significantly enhance and broaden the application of histomorphological assessments in anthropology.
Further model refinement and testing on larger datasets are recommended for future development.