Background:
Bone Age Assessment (BAA) refers to a clinical procedure that aims to
identify a discrepancy between biological and chronological age of an individual by assessing the
bone age growth. Currently, there are two main methods of executing BAA which are known as
Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and
qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability
accuracy and time-consuming. An automatic segmentation can be applied to the radiographs,
providing the physician with more accurate delineation of the carpal bone and accurate quantitative
analysis.
Methods:
In this study, we proposed an image feature extraction technique based on image segmentation
with the fully convolutional neural network with eight stride pixel (FCN-8). A total of
290 radiographic images including both female and the male subject of age ranging from 0 to 18
were manually segmented and trained using FCN-8.
Results and Conclusion:
The results exhibit a high training accuracy value of 99.68% and a loss
rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold
standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78
± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall
qualitative carpal recognition accuracy, respectively.
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images