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Related Concept Videos

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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The upper limb consists of the arm, forearm, wrist, and hand bones. The humerus is the single bone of the upper arm region. Proximally, it has a large, spherical, smooth head that articulates with the glenoid cavity of the scapula to form the glenohumeral or shoulder joint. The margin of the head is the anatomical neck, a residual epiphyseal plate. Laterally it extends to form bony projections called the greater tubercle and the lesser tubercle. Next to the tubercles is the surgical neck, a...

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Related Experiment Video

Updated: May 11, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Shoulder Bone Segmentation with DeepLab and U-Net.

Michael Carl¹, Kaustubh Lall², Darren Pai³

¹General Electric Healthcare, Menlo Park, CA.

Osteology (Basel, Switzerland)

|October 30, 2024

Summary

This summary is machine-generated.

This study shows U-Net deep learning model achieves higher accuracy in segmenting humeral bone on zero echo time MRI scans compared to DeepLab, aiding pre-surgical planning.

Keywords:

DeepLab MRI U-Net ZTE glenohumeral glenoid humeral head image processing

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Accurate 3D bone morphology evaluation of the glenohumeral joint is crucial for pre-surgical planning.
Zero echo time (ZTE) magnetic resonance imaging (MRI) offers excellent bone contrast, potentially replacing computed tomography.
Automated segmentation of shoulder anatomy is needed for detailed assessment and surgical preparation.

Purpose of the Study:

To compare the performance of DeepLab and U-Net deep learning models for automated segmentation of humeral bone and acetabulum on ZTE MRI.
To evaluate the potential of deep learning for improving shoulder MRI analysis.

Main Methods:

Two deep learning models, DeepLab and 2D U-Net, were trained and validated for segmentation on axial ZTE MRI scans of normal shoulders.
The models were trained on 31 shoulders and tested on 13 shoulders.
Performance was quantified using the Dice score.

Main Results:

Both models provided visually satisfactory segmentation of the humeral bone.
U-Net (88% Dice score) significantly outperformed DeepLab (81% Dice score) in humeral segmentation accuracy (p<0.05).
U-Net showed a slight over-estimation, while DeepLab showed under-estimation compared to ground truth.

Conclusions:

U-Net demonstrates superior performance for automated humeral bone segmentation on ZTE MRI.
Implementing U-Net on an MRI console allows for push-button deep learning segmentation processing.
This approach has the potential to enhance shoulder MR evaluations by reducing manual post-processing and aiding visualization of the glenohumeral joint.