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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...

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Updated: Sep 6, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Statistical Shape Model of the Temporal Bone Using Segmentation Propagation.

Andy S Ding, Alexander Lu, Zhaoshuo Li¹

¹Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland.

Otology & Neurotology : Official Publication of the American Otological Society, American Neurotology Society [And] European Academy of Otology and Neurotology

|June 28, 2022

Summary

This summary is machine-generated.

Automated statistical shape models (SSMs) reveal significant anatomical variations in the human temporal bone, including the malleus, facial nerve, and bony labyrinth. These findings enhance understanding of inter-patient differences for surgical planning and research.

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A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

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Published on: July 2, 2014

Area of Science:

Medical Imaging
Anatomy
Biomedical Engineering

Background:

Inter-patient anatomical variation in the temporal bone is not well understood.
Statistical shape models (SSMs) quantify anatomical variation but are labor-intensive to create manually.
This study introduces automated image-registration techniques for developing temporal bone SSMs.

Purpose of the Study:

To develop statistical shape models (SSMs) of human temporal bone anatomy using automated image registration.
To quantify and describe inter-patient anatomical variation in key temporal bone structures.
To provide a foundation for further research into factors influencing anatomical variability.

Main Methods:

Fifty-three temporal bone CT scans were used to generate SSMs.
Automated segmentation of the malleus, incus, stapes, bony labyrinth, and facial nerve was performed using 3D Slicer.
MATLAB was utilized to construct SSMs, and principal component analysis described shape variation.

Main Results:

Novel modes of anatomical variation were identified in the middle and inner ear structures.
Malleus variability included manubrium length and axial rotation.
Facial nerve and bony labyrinth variations were quantified in terms of genu angles, canal orientation, and cochlear orientation.

Conclusions:

Automated SSMs of temporal bone anatomy offer valuable insights into clinically relevant inter-patient variability.
The elucidated anatomical variations can inform surgical approaches and deepen understanding of function and pathophysiology.
These models facilitate further investigations into variations related to demographic and geographic factors.