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Carpal Bone Segmentation Using Fully Convolutional Neural Network

[ Vol. 15 , Issue. 10 ]

Author(s):

Liang Kim Meng, Azira Khalil, Muhamad Hanif Ahmad Nizar , Maryam Kamarun Nisham, Belinda Pingguan-Murphy, Yan Chai Hum, Maheza Irna Mohamad Salim and Khin Wee Lai*   Pages 983 - 989 ( 7 )

Abstract:


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.

Keywords:

Image, segmentation, bone, assessment, extraction, convolutional neural network.

Affiliation:

Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Faculty of Science and Technology, Islamic Science University of Malaysia, 71800, Nilai, Negeri Sembilan, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor Darul Ehsan, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor, 81310, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur

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