UMN Knowledge Center

Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks

Al-Kafri, Ala S. and Sudirman, Sud and Hussain, Abir and Al-Jumeily, Dhiya and Natalia, Friska and Meidia, Hira and Afriliana, Nunik and Al-Rashdan, Wasfi (2019) Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks. IEEE Access ( Volume: 7). pp. 43487-43501. ISSN 2169-3536

Full text not available from this repository.
Official URL: https://ieeexplore.ieee.org/abstract/document/8678...

Abstract

We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of four important regions in the lumbar spine, to be used as the training and test images to develop classification models for segmentation. We developed two novel metrics, namely confidence, and consistency, to assess the quality of the ground truth dataset through a derivation of the Jaccard Index. We experimented with semantic segmentation of our dataset using SegNet. Our evaluation of the segmentation and the delineation results show that our proposed methodology produces a very good performance as measured by several contour-based and region-based metrics. In addition, using the Cohen's kappa and frequency-weighted confidence metrics, we can show that 1) the model's performance is within the range of the worst and the best manual labeling results and 2) the ground-truth dataset has an excellent inter-rater agreement score. We also presented two representative delineation results of the worst and best segmentation based on their BF-score to show visually how accurate and suitable the results are for computer-aided-diagnosis purposes.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming (Algorithm, Programming Language, Applications, Software, Data Security)
600 Technology (Applied Sciences) > 610 Medicine and Health > 617 Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 11 Oct 2021 16:30
Last Modified: 11 Oct 2021 16:30
URI: http://kc.umn.ac.id/id/eprint/18656

Actions (login required)

View Item View Item