Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions

Natalia, F, Sudirman, S, Ruslim, D and Al-Kafri, A (2024) Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions. PLoS One, 19 (5). pp. 1-27. ISSN 1932-6203

Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions.pdf - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview


Many lumbar spine diseases are caused by defects or degeneration of lumbar intervertebral discs (IVD) and are usually diagnosed through inspection of the patient’s lumbar spine MRI. Efficient and accurate assessments of the lumbar spine are essential but a challenge due to the size of the clinical radiologist workforce not keeping pace with the demand for radiology services. In this paper, we present a methodology to automatically annotate lumbar spine IVDs with their height and degenerative state which is quantified using the Pfirrmann grading system. The method starts with semantic segmentation of a mid-sagittal MRI image into six distinct non-overlapping regions, including the IVD and vertebrae regions. Each IVD region is then located and assigned with its label. Using geometry, a line segment bisecting the IVD is determined and its Euclidean distance is used as the IVD height. We then extract an image feature, called self-similar color correlogram, from the nucleus of the IVD region as a representation of the region’s spatial pixel intensity distribution. We then use the IVD height data and machine learning classification process to predict the Pfirrmann grade of the IVD. We considered five different deep learning networks and six different machine learning algorithms in our experiment and found the ResNet-50 model and Ensemble of Decision Trees classifier to be the combination that gives the best results. When tested using a dataset containing 515 MRI studies, we achieved a mean accuracy of 88.1%.

Item Type: Article
Uncontrolled Keywords: Magnetic Resonance Imaging; Vertebrae; Spine; Imaging Techniques; Machine Learning; Radiology; Decision Tree Learning; General Science & Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Public Library of Science
SWORD Depositor: A Symplectic
Date Deposited: 13 May 2024 11:10
Last Modified: 13 May 2024 11:10
DOI or ID number: 10.1371/journal.pone.0302067
Editors: Akeda, K
URI: https://researchonline.ljmu.ac.uk/id/eprint/23242
View Item View Item