Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset
Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we prese...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2020/8460493 |
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| author | Xiaoying Pan Yizhe Zhao Hao Chen De Wei Chen Zhao Zhi Wei |
| author_facet | Xiaoying Pan Yizhe Zhao Hao Chen De Wei Chen Zhao Zhi Wei |
| author_sort | Xiaoying Pan |
| collection | DOAJ |
| description | Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance. |
| format | Article |
| id | doaj-art-2cb4a0f7738b43bf9f95b67137b801c3 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-2cb4a0f7738b43bf9f95b67137b801c32025-02-03T06:04:38ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962020-01-01202010.1155/2020/84604938460493Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray DatasetXiaoying Pan0Yizhe Zhao1Hao Chen2De Wei3Chen Zhao4Zhi Wei5School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, Mississippi 39406, USADepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USABone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance.http://dx.doi.org/10.1155/2020/8460493 |
| spellingShingle | Xiaoying Pan Yizhe Zhao Hao Chen De Wei Chen Zhao Zhi Wei Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset International Journal of Biomedical Imaging |
| title | Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset |
| title_full | Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset |
| title_fullStr | Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset |
| title_full_unstemmed | Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset |
| title_short | Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset |
| title_sort | fully automated bone age assessment on large scale hand x ray dataset |
| url | http://dx.doi.org/10.1155/2020/8460493 |
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