Paul H Yi1,2, Tae Kyung Kim1,2, Jinchi Wei2, Jiwon Shin2, Ferdinand K Hui1,2, Haris I Sair1,2, Gregory D Hager2, Jan Fritz3,4. 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.
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.
Authors: Daniel Shu Wei Ting; Carol Yim-Lui Cheung; Gilbert Lim; Gavin Siew Wei Tan; Nguyen D Quang; Alfred Gan; Haslina Hamzah; Renata Garcia-Franco; Ian Yew San Yeo; Shu Yen Lee; Edmund Yick Mun Wong; Charumathi Sabanayagam; Mani Baskaran; Farah Ibrahim; Ngiap Chuan Tan; Eric A Finkelstein; Ecosse L Lamoureux; Ian Y Wong; Neil M Bressler; Sobha Sivaprasad; Rohit Varma; Jost B Jonas; Ming Guang He; Ching-Yu Cheng; Gemmy Chui Ming Cheung; Tin Aung; Wynne Hsu; Mong Li Lee; Tien Yin Wong Journal: JAMA Date: 2017-12-12 Impact factor: 56.272
Authors: Paul H Yi; Tae Kyung Kim; Jinchi Wei; Xinning Li; Gregory D Hager; Haris I Sair; Jan Fritz Journal: Skeletal Radiol Date: 2020-05-15 Impact factor: 2.199
Authors: Jinchi Wei; David Li; David C Sing; JaeWon Yang; Indeevar Beeram; Varun Puvanesarajah; Craig J Della Valle; Paul Tornetta; Jan Fritz; Paul H Yi Journal: Emerg Radiol Date: 2022-05-24