Cited 3 time in webofscience Cited 4 time in scopus

Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Title
Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks
Authors
Won, DongkyuLee, Hyun-JooLee, Suk-JoongPark, Sang Hyun
DGIST Authors
Park, Sang Hyun
Issue Date
2020-06
Citation
Spine, 45(12), 804-812
Type
Article
Article Type
Article
Author Keywords
deep convolutional neural networksgradinglumbarspinal stenosis
Keywords
CLASSIFICATIONAREA
ISSN
0362-2436
Abstract
Objective. This study aims to verify the feasibility of a computer-assisted spine stenosis grading system by comparing the diagnostic agreement between two experts and the agreement between the experts and trained artificial CNN classifiers. Summary of Background Data. Spinal stenosis grading is important; however, it is tedious job to check the MR images slide by slide to classify patient grades often having different opinions regarding the final diagnosis. Methods. For 542 L4-5 axial MR images, two experts independently localized the center position of the spine canal and graded the status. Two CNN classifiers each trained with the grading label made by the two experts were validated using 10-fold cross-validation. Each classifier consisted of a CNN detection model responsible for the localization of patches near the canal and a classification CNN model to predict the spinal stenosis status in the localized patches. Faster R-CNN was used for the detection model whereas VGG network was used for the classification model. A comparison in grading agreement was carried out between the two experts as well as that of the experts and the prediction results generated by the CNN models. Results. Grading agreement between the experts was 77.5% and 75% in terms of accuracy and F1 scores. The agreement between the first expert and the model trained with the labels of the first expert was 83% and 75.4%, respectively. The agreement between the second expert and the model trained with the labels of the second expert was 77.9% and 74.9%. The differences between the two experts were significant, whereas the differences between each expert and the trained models were not significant. Conclusion. We indeed confirmed that automatic diagnosis using deep learning may be feasible for spinal stenosis grading. © 2020 Wolters Kluwer Health, Inc.
URI
http://hdl.handle.net/20.500.11750/12047
DOI
10.1097/brs.0000000000003377
Publisher
Lippincott Williams and Wilkins
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
There are no files associated with this item.
Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab1. Journal Articles


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