Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

Abstract

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.

Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

“…Recently, the success of CNNs for medical image processing has been reported [18][19][20]. Deep learning methods based on CNNs have been suggested for image preprocessing and feature extraction in BAA tasks, such as fine-tuned CNNs [10], methods based on visual geometry groups (VGGs) [11], UNets for segmentation [12,13], deep residual network (ResNet)-based models [14], and CNNs with attention mechanisms [15]. Moreover, scholars have proposed new ideas for solving the problem of BAA.…”

“…In the BAA task of hand X-ray image, there are several effective preprocessing methods, for example, bone segmentation [11,12], convex-hull based method [21], and so on.…”

“…Automatic computer methods have played important roles in medical image processing, as well as in BAA [5][6][7][8][9]. Moreover, deep learning methods play an important role in BAA [10][11][12][13][14][15][16][17].…”

Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

“…The performance of deep learning methods to estimate the bone age is presented by Larson et al [42]. Also, the large Scale Hand X-Ray Dataset bone age estimation is proposed by Pan et al [43]. The other researcher use two step method in bone age estimation.…”

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

“…But because of patches' overlap, the network was really time-consuming. The U-Net network [ 20 ], which was originally proposed for medical image segmentation and could be utilized for segmentation problems with limited amounts of data [ 21 ], was applied to predict hand masks [ 22 ]. Another network VGG-16 [ 23 ] was integrated with U-Net as an encoder-decoder structure to obtain hand mask [ 24 ].…”