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

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Carpal Bone Segmentation Using Fully Convolutional Neural Network.

Liang Kim Meng¹, Azira Khalil², Muhamad Hanif Ahmad Nizar¹

¹Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.

Current Medical Imaging Reviews

|February 4, 2020

Summary

This study introduces an automated segmentation technique for bone age assessment (BAA) using FCN-8, improving carpal bone analysis accuracy and efficiency over manual methods.

Keywords:

Image assessment bone convolutional neural network extraction segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Bone Age Assessment (BAA) is crucial for identifying discrepancies between biological and chronological age.
Current methods (Greulich-Pyle, Tanner-Whitehouse) rely on manual radiographic analysis, leading to variability and time consumption.
Automated segmentation offers potential for more accurate carpal bone delineation and quantitative analysis.

Purpose of the Study:

To propose and evaluate an automated image feature extraction technique for Bone Age Assessment.
To enhance the accuracy and efficiency of carpal bone segmentation in radiographic images.
To develop a quantitative analysis method for BAA using deep learning.

Main Methods:

Utilized a fully convolutional neural network with eight stride pixels (FCN-8) for image segmentation.
Trained the FCN-8 model on 290 manually segmented hand and wrist radiographs (ages 0-18).
Compared the automated segmentation results against gold standard ground truth images.

Main Results:

Achieved high training accuracy (99.68%) and low loss rate (0.008619) over 50 epochs.
Demonstrated an automated segmentation accuracy of 0.78 ± 0.06 (Dice Coefficient) and 1.56 ± 0.30 mm (Hausdorff Distance).
Reported an overall qualitative carpal recognition accuracy of 98.02% for the automated technique.

Conclusions:

The proposed FCN-8 based automated segmentation technique significantly improves BAA accuracy.
This method offers a more objective and efficient alternative to traditional manual BAA techniques.
Automated quantitative analysis of carpal bones can enhance clinical decision-making in BAA.