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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: Aug 23, 2025

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

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

Published on: November 30, 2022

Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net.

D Morita¹, S Mazen², S Tsujiko³

¹Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan.

International Journal of Oral and Maxillofacial Surgery

|November 3, 2022

Summary

This summary is machine-generated.

Deep learning models can automatically segment facial bones from CT scans for surgical planning. This U-Net model achieved high accuracy for the mandible and zygomatic bones, aiding maxillofacial surgery applications.

Keywords:

Artificial intelligence Deep learning Facial bones Maxillofacial surgery X-ray computed tomography

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Last Updated: Aug 23, 2025

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Published on: November 30, 2022

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Maxillofacial Surgery

Background:

Deep learning (DL) is increasingly used in medical imaging.
Previous DL segmentation focused on single facial bone areas.
Multi-area facial bone segmentation is less explored.

Purpose of the Study:

Investigate automatic segmentation of facial bones into eight areas using a U-Net model.
Facilitate virtual surgical planning (VSP) and computer-aided design and manufacturing (CAD/CAM) in maxillofacial surgery.

Main Methods:

A U-Net deep learning model was employed.
Computed tomography (CT) data from 50 patients were used for training.
Five-fold cross-validation was performed to assess model performance.

Main Results:

The DL model achieved successful automatic segmentation of facial bones in all cases.
Mean Dice coefficient was 0.897 ± 0.077, and mean ASSD was 1.168 ± 1.962 mm.
High accuracy was observed for the mandible and zygomatic bones, suitable for VSP and CAD/CAM.

Conclusions:

The U-Net model demonstrates high potential for multi-area facial bone segmentation in maxillofacial surgery.
Accuracy for certain areas like teeth requires further improvement due to artifacts and anatomical variations.
Multi-institutional validation with diverse populations is necessary for broader clinical application.