ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.
Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine.



To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense.



12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist.



The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies 'Evidence Hotspots' that are the voxels that most contribute to the degradation scores.



Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts.



Level 3.
Department of Engineering Science, University of Oxford, Oxford, UK.
Amir
A
Jamaludin
Department of Engineering Science, University of Oxford, Oxford, UK.
Meelis
M
Lootus
Mirada Medical, Oxford, UK.
Timor
T
Kadir
Department of Engineering Science, University of Oxford, Oxford, UK.
Andrew
A
Zisserman
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Jill
J
Urban
Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada.
Michele C
MC
Battié
Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK. jeremy.fairbank@ndorms.ox.ac.uk.|Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Trust, Oxford, UK. jeremy.fairbank@ndorms.ox.ac.uk.|St Lukes Hospital, Latimer Road, Oxford, OX3 7PF, UK. jeremy.fairbank@ndorms.ox.ac.uk.
Jeremy
J
Fairbank
Spinal Studies, RJAH Orthopaedic Hospital Foundation Trust and ISTM (Keele University), Oswestry, UK.
Iain
I
McCall
D001853:Bone Marrow / Q000000981:diagnostic imaging; D006801:Humans; D007403:Intervertebral Disc / Q000000981:diagnostic imaging*; D055959:Intervertebral Disc Degeneration / Q000000981:diagnostic imaging; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D000072177:Radiologists; D013130:Spinal Stenosis / Q000000981:diagnostic imaging; D013168:Spondylolisthesis / Q000000981:diagnostic imaging
Automated grading; Deep learning; Disc analysis; Disc bulge; Disc classification; Disc detection; Disc herniation; Modic changes; Pfirrmann grading; Spondylolisthesis; Vertebrae analysis