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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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Automated multiclass bone segmentation using deep learning: implications for templating in radial head replacement.

Ausberto R Velasquez Garcia¹, Linjun Yang², Hiroki Nishikawa³

¹Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA; Clinica Universidad de los Andes, Department of Orthopedic Surgery, Santiago, Chile.

Journal of Shoulder and Elbow Surgery

|February 20, 2026

Summary

This summary is machine-generated.

A deep learning model significantly speeds up bone segmentation for radial head arthroplasty (RHA) planning. This AI tool offers high accuracy, improving efficiency and precision in preoperative 3D templating for RHA.

Keywords:

Deep learning artificial intelligence nnU-Net preoperative planning radial head arthroplasty templating upper extremity bone segmentation

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Area of Science:

Orthopedic Surgery
Medical Imaging
Artificial Intelligence

Background:

Preoperative 3D templating enhances surgical accuracy in radial head arthroplasty (RHA).
Current segmentation methods are time-consuming and variable.
Automating segmentation is crucial for efficient RHA planning.

Purpose of the Study:

To train and validate an nnU-Net deep learning model for automated multiclass bone segmentation.
To assess the accuracy and efficiency of the nnU-Net model for RHA templating.
To support 3D bone templating in RHA through accurate segmentation.

Main Methods:

Trained and evaluated an nnU-Net model on 93 upper extremity CT scans.
Used Dice Similarity Coefficient (DSC) and Hausdorff Distance for accuracy assessment.
Compared 3D bone models using Mean Surface Distance (MSD) and Root Mean Squared Error (RMSE).

Main Results:

nnU-Net achieved high segmentation accuracy (DSC: 0.95-0.99) for humerus, ulna, and radius.
Mean Surface Distance (MSD) and Root Mean Squared Error (RMSE) were consistently low (<0.2 mm).
Segmentation time reduced from 78 min (manual) to 3 min per scan with nnU-Net.

Conclusions:

The nnU-Net model offers a fast, reliable solution for bone segmentation in RHA.
Achieved high accuracy for cortical and non-cortical bone regions, meeting clinical needs.
Demonstrates clinical feasibility for improving efficiency and precision in RHA preoperative planning.