Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc.

Autor: Sayed MA; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh., Rahman GMM; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh. mahmud@bme.kuet.ac.bd., Islam MS; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh. sheraj_kuet@eee.kuet.ac.bd., Islam MA; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh., Park J; Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, 89557, USA.; School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, Canada., Ahmed H; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh., Hossain A; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh., Shahrior R; Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2025 Jan 02; Vol. 15 (1), pp. 593. Date of Electronic Publication: 2025 Jan 02.
DOI: 10.1038/s41598-024-84301-7
Abstrakt: Magnetic resonance (MR) images are commonly used to diagnose prolapsed lumbar intervertebral disc (PLID). However, for a computer-aided diagnostic (CAD) system, distinguishing between pathological abnormalities of PLID in MR images is a challenging and intricate task. Here, we propose a comprehensive model for the automatic detection and cropping of regions of interest (ROI) from sagittal MR images using the YOLOv8 framework to solve this challenge. We also propose weighted average ensemble (WAE) classification and segmentation models for the classification and the segmentation, respectively. YOLOv8 has good detection accuracy for both the lumbar region (mAP50 = 99.50%) and the vertebral disc (mAP50 = 99.40%). The use of ROI approaches enhances the accuracy of individual models. Specifically, the classification accuracy of the WAE classification model reaches 97.64%, while the segmentation model achieves a Dice value of 95.72%. This automatic technique would improve the diagnostic process by offering enhanced accuracy and efficiency in the assessment of PLID.
Competing Interests: Competing interests: The authors declare no competing interests. Informed consent statement: Written informed consent from the patients is waived due to the retrospective nature of the data collection and the use of de-identified MR images, and full ethical approval has been granted.
(© 2024. The Author(s).)
Databáze: MEDLINE
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