Efficient segmentation of lumbar intervertebral disc from MR images

M, Leena Silvoster; Kumar, Retnaswami Mathusoothana S.

doi:10.1049/iet-ipr.2019.0971

Cited by 7 publications

(4 citation statements)

References 48 publications

(45 reference statements)

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“…Image registration quality was evaluated for all 240 registrations using Dice similarity coefficient (where a Dice of 0 means there was zero overlap of the objects, while 1 means perfect overlap of the objects), where the loaded image's manual disc segmentation was transformed to the Reference image via the calculated registration and then compared to the Reference image's manual disc segmentation. Dice coefficient was high for this study, indicating strong registrations (0.92 ± 0.03), comparable to the best of prior literature 55,56 …”

Section: Methodssupporting

confidence: 88%

“…high for this study, indicating strong registrations (0.92 ± 0.03), comparable to the best of prior literature. 55,56 The anatomic disc coordinate system was defined for each disc with its left-right axis the same as the left-right patient axis from the MRI scanner. Maintaining both this constraint and orthogonality, the A-P and axial (superior-inferior) axes were set to align as closely as possible with the second and third principal axes of the segmented disc volume determined via principal component analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Dice coefficient was high for this study, indicating strong registrations (0.92 ± 0.03), comparable to the best of prior literature. 55 , 56 …”

Section: Methodsmentioning

confidence: 99%

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MRI‐based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading

et al. 2023

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Background Intervertebral disc degeneration is often implicated in low back pain; however, discs with structural degeneration often do not cause pain. It may be that disc mechanics can provide better diagnosis and identification of the pain source. In cadaveric testing, the degenerated disc has altered mechanics, but in vivo, disc mechanics remain unknown. To measure in vivo disc mechanics, noninvasive methods must be developed to apply and measure physiological deformations. Aim Thus, this study aimed to develop methods to measure disc mechanical function via noninvasive MRI during flexion and extension and after diurnal loading in a young population. This data will serve as baseline disc mechanics to later compare across ages and in patients. Materials & Methods To accomplish this, subjects were imaged in the morning in a reference supine position, in flexion, in extension, and at the end of the day in a supine position. Disc deformations and vertebral motions were used to quantify disc axial strain, changes in wedge angle, and anterior–posterior (A‐P) shear displacement. T 2 weighted MRI was also used to evaluate disc degeneration via Pfirrmann grading and T 2 time. All measures were then tested for effect of sex and disc level. Results We found that flexion and extension caused level‐dependent strains in the anterior and posterior of the disc, changes in wedge angle, and A‐P shear displacements. Flexion had higher magnitude changes overall. Diurnal loading did not cause level‐dependent strains but did cause small level‐dependent changes in wedge angle and A‐P shear displacements. Discussion Correlations between disc degeneration and mechanics were largest in flexion, likely due to the smaller contribution of the facet joints in this condition. Conclusion In summary, this study established methods to measure in vivo disc mechanical function via noninvasive MRI and established a baseline in a young population that may be compared to older subjects and clinical disorders in the future.

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Section: Methodssupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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MRI‐based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading

et al. 2023

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“…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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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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Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis

et al. 2023

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Introduction Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists. Methods A PRISMA systematic review protocol was developed and four electronic databases and reference lists were searched. Strict inclusion and exclusion criteria were defined. A PROBAST risk of bias and applicability analysis was performed. Results 1350 articles were extracted. Duplicates were removed and title and abstract searching identified original research articles that used machine learning (ML) algorithms to identify disc degeneration, herniation, bulge and Modic change from MRIs. 27 studies were included in the review; 25 and 14 studies were included multi-variate and bivariate meta-analysis, respectively. Studies used machine learning algorithms to assess LDD, disc herniation, bulge and Modic change. Models using deep learning, support vector machine, k-nearest neighbors, random forest and naïve Bayes algorithms were included. Meta-analyses found no differences in algorithm or classification performance. When algorithms were tested in replication or external validation studies, they did not perform as well as when assessed in developmental studies. Data augmentation improved algorithm performance when compared to models used with smaller datasets, there were no performance differences between augmented data and large datasets. Discussion This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.

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Efficient segmentation of lumbar intervertebral disc from MR images

Cited by 7 publications

References 48 publications

MRI‐based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading

MRI‐based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis

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