Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

Kafri, Ala S. Al; Sudirman, Sud; Hussain, Abir Jaafar; Al‐Jumeily, Dhiya; Fergus, Paul; Natalia, Friska; Meidia, Hira; Afriliana, Nunik; Sophian, Ali; Al-Jumaily, Mohammed; Al-Rashdan, Wasfi; Bashtawi, Mohammad

doi:10.1109/cec.2018.8477893

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…In particular, spinal stenosis, which causes chronic low back pain, is caused by the canal in 3 vertebrae in the lumbar part [5]. Here; AAP segmentation of the intervertebral disc, posterior part (posterior element), nerve bundle (thecal sac) and cavity is a time-consuming process as well as requiring serious expertise.…”

Section: Figure 2 Vertebrae Disc and Canalmentioning

confidence: 99%

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Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Altun

Alkan

2023

Gazi University Journal of Science

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Lumbar spinal stenosis is a disease with negative consequences and usually occurs in 3 vertebrae, disc and canal located in the lower back. In this region, the nerves in the canal can be exposed to pressure for various reasons, and diseases occur. Surgical operation is required to treat canal narrowing, and the exact location and size of the spinal stenosis is vital importance for the operation. The UNet model, which is an example of this network, can be further deeper using different deep learning networks. In this study, it is aimed to be the basis for the creation of a system that helps in the diagnosis of canal stenosis by using a deeper network. The ResUNET model, in which ResNet is used as the backbone, achieved an average IoU of 0.987. This result reveals that MR images can be used in segmentation for the diagnosis of Lumbar spinal stenosis.

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Section: Figure 2 Vertebrae Disc and Canalmentioning

confidence: 99%

“…Al-Kafri et al [5] Segmentation Silvestor et al [26] Efficient segmentation of lumbar intervertebral disc from MR images IET Image Processing 2020 dice similarity index of 92.4%…”

Section: Successesmentioning

confidence: 99%

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Altun

Alkan

2023

Gazi University Journal of Science

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“…We have demonstrated previously in [19] that an accurate and consistent delineation of these boundaries cannot be performed just through an application of an edge detection algorithm directly on the MRI image. Instead, the image needs to be segmented beforehand.…”

Section: B Image Labels and Ground-truth Datamentioning

confidence: 99%

“…It is important to note that we do not use the slices of all five lumbar IVDs, but instead, we use the slice of the last three only. The rationale of this was provided in our previous work [19].…”

Section: B Image Labels and Ground-truth Datamentioning

confidence: 99%

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

et al. 2019

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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 contourbased 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.

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“…Recent advances in medical image processing allow the application of computer algorithms to help radiologists carried out this procedure. Some of these algorithms work only on midsagittal MRI images [1][2][3] whereas some others work only on traverse images that cut through the mid-height of an intervertebral disc (IVD) [4][5][6][7][8]. Since a patient's data repository contains more than just these specific images, the process to select suitable images as inputs to these algorithms is often done manually.…”

Section: Introductionmentioning

confidence: 99%

Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection Using Transfer Learning of a Deep Convolutional Neural Network

Natalia

Sudirman

2022

Intelligent Sustainable Systems

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Analysis of sagittal lumbar spine MRI images remains an important step in automated detection and diagnosis of lumbar spinal stenosis. There are numerous algorithms proposed in the literature that can measure the condition of lumbar intervertebral discs through analysis of the lumbar spine in the sagittal view. However, these algorithms rely on using suitable sagittal images as their inputs. Since an MRI data repository contains more than just these specific images, it is, therefore, necessary to employ an algorithm that can automatically select such images from the entire repository. In this paper, we demonstrate the application of an image classification method using deep convolutional neural networks for this purpose. Specifically, we use a pre-trained Inception-ResNet-v2 model and retrain it using two sets of T1-weighted and T2-weighted images. Through our experiment, we can conclude that this method can reach a performance level of 0.91 and 0.93 on the T1 and T2 datasets, respectively when measured using the accuracy, precision, recall, and f1-score metrics. We also show that the difference in performance between using the two modalities is statistically significant and using T2-weighted images is preferred over using T1weighted images.

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Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

Cited by 15 publications

References 22 publications

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

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

Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection Using Transfer Learning of a Deep Convolutional Neural Network

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