Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

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 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

Pediatric Bone Age Analysis and Brain Disease Prediction for Computer-Aided Diagnosis

Recent advances in 3D scanning technology have led to a widespread use of 3D shapes in a multitude of fields, including computer vision and medical imaging. These shapes are, however, often contaminated by noise, which needs to be removed or attenuated in order to ensure high-quality 3D shapes for subsequent use in downstream tasks. On the other hand, the availability of largescale pediatric hand radiographs and brain imaging benchmarks has sparked a surge of interest in designing efficient techniques for bone age assessment and brain disease prediction, which are fundamental problems in computer-aided diagnosis. Bone age is an effective metric for assessing the skeletal and biological maturity of children, while understanding how the brain develops is crucial for designing prediction models for the classification of brain disorders. In this thesis, we present a feature-preserving framework for carpal bone surface denoising in the graph signal processing setting. The proposed denoising framework is formulated as a constrained optimization problem with an objective function comprised of a fidelity term specified by a noise model and a regularization term associated with data prior. We show through experimental results that our approach can remove noise effectively while preserving the nonlinear features of surfaces, such as curved surface regions and fine details. Moreover, recovering high quality surfaces from noisy carpal bone surfaces is of paramount importance to the diagnosis of wrist pathologies, such as arthritis and carpal tunnel syndrome. We also introduce a deep learning approach to pediatric bone age assessment using instance segmentation and ridge regression. This approach is comprised of two intertwined stages. In the first stage, we employ an image annotation and instance segmentation model to extract and separate different regions of interests in an image. In the second stage, we leverage the power of transfer learning by designing a deep neural network with a ridge regression output layer. For the classification of brain disorders, we propose an aggregator normalization graph convolutional network by exploiting aggregation in graph sampling, skip connections and identity mapping. We also integrate both imaging and non-imaging features into the graph nodes and edges, respectively, with the aim of augmenting predictive capabilities. We validate our proposed approaches through extensive experiments on various benchmark datasets, demonstrating competitive performance in comparison with strong baseline methods

Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children

The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (≤5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, p = 0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, p = 0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.ope

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research