Format

Send to

Choose Destination
Pediatr Radiol. 2019 Jul;49(8):1066-1070. doi: 10.1007/s00247-019-04408-2. Epub 2019 Apr 30.

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Author information

1
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
2
Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
3
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA. jfritz9@jhmi.edu.
4
Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA. jfritz9@jhmi.edu.

Abstract

BACKGROUND:

An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imaging data sets for machine learning purposes. Deep learning is a potential tool for this purpose.

OBJECTIVE:

To develop and test the performance of deep convolutional neural networks (DCNN) for the automated classification of pediatric musculoskeletal radiographs by anatomical area.

MATERIALS AND METHODS:

We utilized a database of 250 pediatric bone radiographs (50 each of the shoulder, elbow, hand, pelvis and knee) to train 5 DCNNs, one to detect each anatomical region amongst the others, based on ResNet-18 pretrained on ImageNet (transfer learning). For each DCNN, the radiographs were randomly split into training (64%), validation (12%) and test (24%) data sets. The training and validation data sets were augmented 30 times using standard preprocessing methods. We also tested our DCNNs on a separate test set of 100 radiographs from a single institution. Receiver operating characteristics (ROC) with area under the curve (AUC) were used to evaluate DCNN performances.

RESULTS:

All five DCNN trained for classification of the radiographs into anatomical region achieved ROC AUC of 1, respectively, for both test sets. Classification of the test radiographs occurred at a rate of 33 radiographs per s.

CONCLUSION:

DCNNs trained on a small set of images with 30 times augmentation through standard processing techniques are able to automatically classify pediatric musculoskeletal radiographs into anatomical region with near-perfect to perfect accuracy at superhuman speeds. This concept may apply to other body parts and radiographic views with the potential to create an all-encompassing semantic-labeling DCNN.

KEYWORDS:

Artificial intelligence; Children; Deep learning; Machine learning; Musculoskeletal; Radiography; Semantic labeling

PMID:
31041454
DOI:
10.1007/s00247-019-04408-2

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center