Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

By Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A Kalinin, Alexey A. Shvets. 2017

128 citations recorded by the source corpus.

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 the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition is consisted of 12.6k radiological images. Each radiograph in this dataset is an image of a left hand labeled by the bone age and the sex of a patient. Our approach utilizes several deep neural network architectures 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 the 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.

Subfields: Archeology, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Albumentations: fast and flexible image augmentations (2018, background): “Similarly, the use of medical image augmentations helped to improve the results of segmentation of hand radiographs and bone age assessment [15].”
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018, methodology): “We refer reader to Iglovikov et al. (2017a) for further details.”
Camera Model Identification Using Convolutional Neural Networks (2018, background): “Problems in satellite [1], [2], [3], [4], medical [5], [6], [7], [8] or any other imagery are successfully tackled with these techniques, routinely beating human performance in many tasks, including classiﬁcation, segmentation, and detection.”
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018, unknown): “.”
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation (2018, methodology): “ information to higher resolution layers. This type of network architecture proven themselves in binary image segmentation competitions such as satellite image analysis [5] and medical image analysis [6], [7] and other [9]. In this paper, we show how the performance of U-Net can be easily improved b”
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for breast cancer histology image analysis [17], bone disease prediction [21] and age assessment [10], and other problems [5].”
Breast Tumor Cellularity Assessment using Deep Neural Networks (2019, methodology): “larity into 101 class using regular bins with thresholds 0:00;0:01;:::;1:00. The idea of direct regression of an image into continuous value using CNN is not new. In particular, it was implemented in [16] where the authors use CNN to predict bone age from radiograph. 3.5. Evaluation metrics We asses”