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

Natalia, Friska and Sudirman, Sud and Ruslim, Daniel and Al-KafriI, Ala (2024) Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions. PLOS One, 19 (5). pp. 1-27. ISSN 1932-6203

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Abstract

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 algo- rithms 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 con- taining 515 MRI studies, we achieved a mean accuracy of 88.1%.

Item Type:	Article
Subjects:	600 Technology (Applied Sciences) > 600 Technology > 600 Technology 600 Technology (Applied Sciences) > 610 Medicine and Health
Divisions:	Faculty of Engineering & Informatics > Information System
Depositing User:	Administrator UMN Library
Date Deposited:	01 Aug 2024 04:37
Last Modified:	01 Aug 2024 04:37
URI:	https://kc.umn.ac.id/id/eprint/31162

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