A novel method based on deep learning for herniated lumbar disc segmentation

Mbarki, Wafa; Bouchouicha, Moez; Frizzi, Sébastien; Tshibasu, Frederick; Farhat, Leïla Ben; Sayadi, Mounir

doi:10.1109/ic_aset49463.2020.9318261

Cited by 6 publications

(37 citation statements)

References 37 publications

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“…Mbaki et al reported that the lumbar spine disk classification method is based on deep convolutional neural networks using axial-view MRI [ 14 ]. They also reported using a method based on deep learning for herniated lumbar disk segmentation [ 15 ]. Tsai et al reported a method based on deep learning for the automatic detection of lumbar disk herniation in MRI [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays

Kim

Lee

Jung

et al. 2022

JPM

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Lumbar herniated nucleus pulposus (HNP) is difficult to diagnose using lumbar radiography. HNP is typically diagnosed using magnetic resonance imaging (MRI). This study developed and validated an artificial intelligence model that predicts lumbar HNP using lumbar radiography. A total of 180,271 lumbar radiographs were obtained from 34,661 patients in the form of lumbar X-ray and MRI images, which were matched together and labeled accordingly. The data were divided into a training set (31,149 patients and 162,257 images) and a test set (3512 patients and 18,014 images). Training data were used for learning using the EfficientNet-B5 model and four-fold cross-validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) for the prediction of lumbar HNP was 0.73. The AUC of the ROC for predicting lumbar HNP in L (lumbar) 1-2, L2-3, L3-4, L4-5, and L5-S (sacrum)1 levels were 0.68, 0.68, 0.63, 0.67, and 0.72, respectively. Finally, an HNP prediction model was developed, although it requires further improvements.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays

Kim

Lee

Jung

et al. 2022

JPM

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Section: Introductionmentioning

confidence: 99%

“…The spine is composed of 24 vertebrae including twelve dorsal, five lumbar vertebrae and seven cervical [1]. The intervertebral disc connects each two vertebrae to each other; acting like a shock absorber cushion [1]. The intervertebral discs are composed of two important components: a nucleus pulposus surrounded by a fibrous annulus [1].…”

Section: Introductionmentioning

confidence: 99%

“…The intervertebral disc connects each two vertebrae to each other; acting like a shock absorber cushion [1]. The intervertebral discs are composed of two important components: a nucleus pulposus surrounded by a fibrous annulus [1]. When a part of the nucleus protrudes outside, this is called a herniated disc [1].…”

Section: Introductionmentioning

confidence: 99%

“…The intervertebral discs are composed of two important components: a nucleus pulposus surrounded by a fibrous annulus [1]. When a part of the nucleus protrudes outside, this is called a herniated disc [1]. The latter represents the rupture of the fibrous of an intervertebral disc [1].…”

Section: Introductionmentioning

confidence: 99%

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Herniated Lumbar Disc Generation and Classification Using Cycle Generative Adversarial Networks on Axial View MRI

et al. 2021

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A frequent cause of lower back pain presenting with leg pain is a herniated lumbar intervertebral disc. A herniation or a herniated lumbar disc is a change of position of disc material (nucleus pulpous or annulus fibrosis). Usually, the lower back pain goes away within days or weeks. Regular treatment techniques for lower back pain include medication, exercises, relaxation methods and surgery. Back pain and back problems regularly occur in the lumbar region. The spinal canal is made up of vertebrae; each one protects the spinal nerves. Intervertebral discs and facet joints connect the vertebrae above and below. Groups of muscles and ligaments hold the vertebrae and the discs together. Muscles support the spine and the body weight, and they allow us to move. Pressure can result in excessive wear and tear of the other structures. For example, a common problem in the lower back is disc herniation. In this case, pressure on an intervertebral disc makes its center, the nucleus pulposus, protrude backwards and push against the spinal nerves, leading to lower back pain. Detection and classification are the two most important tasks in computer aided diagnosing systems. Detection of a herniated lumbar disc from magnetic resonance imaging (MRI) is a very difficult task for radiologist. The extraction of herniated discs has been achieved by different approaches such as active contours, region growing, watershed techniques, thresholding and deep learning. In this study, to detect intervertebral disc from axial MRIs we develop a method using generative adversarial networks (GANs), especially the CycleGAN model, to automatically generate and detect intervertebral disc and to classify the type of the herniated lumbar disc such as foraminal or median. We propose to explore the importance of axial view MRI to determine the herniation type. Accurately, GANs and other generative networks have created several ways to tackle different problems well known and challenging of medical image analysis, such as segmentation, reconstruction, data simulation, medical image de-noising, and classification. Moreover, their efficiency to synthesize images and data at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In our case, having a database that contains several images is a very difficult task. In this paper, we put forward a new approach based on GANs, in order to solve the problem of lumbar intervertebral disc images reduction. This method is based especially on CycleGAN. Consequently, the essential objective of our work is to generate and automatically classify the herniation type as foraminal or median using GANs. Our computer aided diagnosis (CAD) system achieved a 97.2% accuracy on our dataset. This result represents a very high-performance results by providing the state of the art and our work utilizing the GANs technique. Our CAD is very effective and efficient for classifying herniations of lumbar intervertebral discs. Therefore, the contribution of this study appears in: firstly, the use of the CycleGAN model based on convolutional layers to detect and classify the herniation type (median or foraminal) in lumbar intervertebral discs, secondly, the use of axial view MRI in order to classify the type of the herniated intervertebral disc. The main objective of this paper is to help radiologists automatically recognize and classify herniated lumbar discs.

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Deep Convolutional Neural Networks Using U-Net for Automatic Intervertebral Disc Segmentation in Axial MRI

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et al. 2022

2022 Innovations in Intelligent Systems and Applications Conference (ASYU)

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A novel method based on deep learning for herniated lumbar disc segmentation

Cited by 6 publications

References 37 publications

Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays

Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays

Herniated Lumbar Disc Generation and Classification Using Cycle Generative Adversarial Networks on Axial View MRI

Deep Convolutional Neural Networks Using U-Net for Automatic Intervertebral Disc Segmentation in Axial MRI

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