Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images

Huang, Jiawei; Shen, Haotian; Wu, Jiali; Hu, Xiaojian; Zhu, Zhiwei; Lv, Xiaoqiang; Liu, Yong; Wang, Yue

doi:10.1016/j.spinee.2019.11.010

Cited by 67 publications

(55 citation statements)

References 27 publications

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“…Therefore, if a new classification should be established, aside from solely and morphologically describing disk degeneration, it should focus on factors that may guide the treatment of patients. For scientific purposes, probably deep learning or machine learning algorithms 17 will supersede the evaluation of spinal degeneration in the near future.…”

Section: Discussionmentioning

confidence: 99%

How Should We Grade Cervical Disk Degeneration? A Comparison of Two Popular Classification Systems

Urbanschitz

Bensler

Merat

et al. 2021

Spine Surg Relat Res

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Introduction Despite being originally developed for the evaluation of lumbar disk degeneration, the Pfirrmann classification has emerged as the most popular classification system for cervical disk degeneration. However, with the Suzuki classification, a new classification system that is specifically tailored for the evaluation of cervical disk disease was introduced. In this study, we aim to evaluate differences in inter- and intraobserver reliability of both classifications in a head-to-head comparison. Methods In total, we have evaluated 120 cervical disks within 40 patients via magnetic resonance imaging according to the Pfirrmann and Suzuki classification. The degree of disk degeneration was evaluated by two independent musculoskeletal radiologists. After 6 months, the classification was reassessed to evaluate the intraobserver reliability. The inter- and intraobserver reliabilities were then calculated using Cohen's kappa. Results The inter- and intraobserver reliability provided a significant agreement between all ratings in Pfirrmann as well as the Suzuki classification (p>0.001). The interobserver reliability was determined to be fair in both the Suzuki classification (κ=0.290) and the Pfirrmann classification (κ=0.265). The intraobserver reliability was substantial in the Suzuki classification (κ=0.798), while it was almost perfect in the Pfirrmann classification (κ=0.858). Conclusions Although not designed for the evaluation of cervical disk degeneration, the Pfirrmann classification yielded equal inter- and higher intraobserver reliability. Both classification systems are viable options for the grading of cervical disk degeneration. While the Pfirrmann classification has the advantage of being better established, the Suzuki classification may be clinically superior due to a better representation of cervical disk degeneration and the consideration of disk bulging for the classification of cervical disk degeneration.

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Section: Discussionmentioning

confidence: 99%

How Should We Grade Cervical Disk Degeneration? A Comparison of Two Popular Classification Systems

Urbanschitz

Bensler

Merat

et al. 2021

Spine Surg Relat Res

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“…There are a wide variety of medical imaging modalities and magnetic resonance imaging (MRI) is majorly applied for clinical diagnosis and prog - nosis. Recently, some studies have demonstrated suc - cessful application of artificial intelligence algorithms for spine medical image segmentation [ 17 , 19 , 20 , 22 , 24 , 27 , 3 3 , 35 , 38 , 47 , 49 ], computer-aided spine diagnosis [ 84 - 87 ], and disease detection and classification [ 10 , 45 ]. In other words, spinal images could be analyzed, processed, and categorized by using neural network.…”

Section: Discussionmentioning

confidence: 99%

A Review on the Use of Artificial Intelligence in Spinal Diseases

et al. 2020

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Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

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“…A neuroradiologist almost always starts his or her inspection of these IVDs in the mid-sagittal view since it can provide a general overview of the lumbar spine. This is reflected also by the popularity of automated inspection of sagittal MRI to detect and localize IVD herniations [ 8 – 12 ]. However, a more accurate assessment of the actual location and extent of the stenosis can only be obtained through inspection of each IVD in axial view [ 13 , 14 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Accurate delineation of region boundaries in an image requires image segmentation, a process that partitions an image into multiple segments, where all pixels belonging to each segment having a set of common characteristics. There are several approaches in the literature that segment lumbar spine MRI images including one for spinal canal detection [ 8 ], and for vertebrae and IVD detection [ 10 , 12 ]. In the more general field of image processing, active contour model is a popular class of image segmentation and boundary delineation method due to its ability to fit a curve to an object boundary.…”

Section: Background and Related Workmentioning

confidence: 99%

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

et al. 2020

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Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm ( p = 0.92) and 0.29 mm ( p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm ( p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

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Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images

Cited by 67 publications

References 27 publications

How Should We Grade Cervical Disk Degeneration? A Comparison of Two Popular Classification Systems

How Should We Grade Cervical Disk Degeneration? A Comparison of Two Popular Classification Systems

A Review on the Use of Artificial Intelligence in Spinal Diseases

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

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