Deep learning for automated skeletal bone age assessment in X-ray images.
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.
Pattern Recognition and Computer Vision (PeRCeiVe) Lab, Department of Electrical, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria, 6 - 95125 - Catania, Italy. Electronic address: cspampin@dieei.unict.it.
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Spampinato
Pattern Recognition and Computer Vision (PeRCeiVe) Lab, Department of Electrical, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria, 6 - 95125 - Catania, Italy. Electronic address: simone.palazzo@dieei.unict.it.
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Palazzo
Pattern Recognition and Computer Vision (PeRCeiVe) Lab, Department of Electrical, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria, 6 - 95125 - Catania, Italy. Electronic address: dgiordan@dieei.unict.it.
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Giordano
Computer Science Department, University of Torino, Corso Svizzera, 185 - 10149 - Torino, Italy. Electronic address: aldinuc@di.unito.it.
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Aldinucci
Department of Orthodontics, University of Catania, Via Santa Sofia, 78 - 95125 - Catania, Italy. Electronic address: rleonardi@unict.it.
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Leonardi
D000293:Adolescent; D000365:Age Determination by Skeleton / Q000379:methods*; D001842:Bone and Bones / Q000000981:diagnostic imaging*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D011859:Radiography / Q000379:methods*; D015203:Reproducibility of Results
Convolutional neural networks; Deep learning for medical images; Greulich and Pyle; Tanner–Whitehouse