Deep Learning for Musculoskeletal Image Analysis


IRMAKÇI İ. , Anwar S. M. , Torigian D. A. , Bagci U.

53rd Asilomar Conference on Signals, Systems, and Computers, California, United States Of America, 3 - 06 November 2019, pp.1481-1485 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/ieeeconf44664.2019.9048671
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.1481-1485
  • Keywords: Musculoskeletal radiology, knee abnormalities, magnetic resonance imaging, deep multi-view classification

Abstract

The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging (MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machine learning, specifically deep learning methods, can be used for rapid and accurate image analysis of MRI scans, an unmet clinical need in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.