Deep Learning-Based Correlation Analysis between Spine Surgery Lumbar Facet Joint and Lumbar Disc Herniation Using Magnetic Resonance Images

Wang, Chao; Yuan, Jian; Huang, Zebin; Shi, Zhicai

doi:10.1155/2021/9623991

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…In recent years, deep neural network-based assistant systems have been widely used in the analysis of spinal medical images, as described in [2], [3], [9], [11]- [20], [24]- [27]. Surgical treatment of spinal injuries often requires the placement of pedicle screws.…”

Section: A Medical Image Segmentationmentioning

confidence: 99%

“…A CCORDING to epidemiological surveys, patients with lumbar disc herniation tend to be younger due to changes in their working styles. The proportion of such patients shows an upward trend year by year, which has seriously affected the regular work and daily life of young people in contemporary society [1]- [3]. Shuyi Lu, Jinhua Liu, Xiaojie Wang, and Yuanfeng Zhou are with the School of Software, Shandong University, Jinan 250100, China (e-mail: lushuyilsy@gmail.com; 734841570a@gmail.com; wangxj811@gmail.com; yfzhou@sdu.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation

Liu

Wang

et al. 2023

IEEE Trans. Med. Imaging

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Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the edge learning ability of the network region of interest (ROI) by fusing features of different scales and dimensions. We also propose a new loss function to improve the convergence of the network to the internal and external edges of the intervertebral disc. Subsequently, we use the ROI bounding box from the feature selection model to crop the original image and calculate the distance features matrix. We then concatenate the cropped CT images, multiscale fusion features, and distance feature matrices and input them into the classification network. Next, the model outputs the classification results and the class activation map (CAM). Finally, the CAM of the original image size is returned to the feature selection network during the upsampling process to achieve collaborative model training. Extensive experiments demonstrate the effectiveness of our method. The model achieved 91.32% accuracy in the lumbar spine disease classification task. In the labelled lumbar disc segmentation task, the Dice coefficient reaches 94.39%. The classification accuracy in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) reaches 91.82%.

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Section: A Medical Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation

Liu

Wang

et al. 2023

IEEE Trans. Med. Imaging

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“…For example, multiple automatic scoring techniques have been implemented in which the degree of IVD degeneration, including the degree of disc herniation is detected using convolutional neural networks (CNN) and semantic segmentation networks. 18 , 19 , 20 , 21 , 22 These techniques can implement algorithms to isolate the tissue of interest 19 , 21 , 23 , 24 and then identify whether the tissue of interest is damaged, bulging, or degenerated. 18 , 19 , 20 , 21 , 22 Similarly, CNNs have been implemented to understand and interpret the severity of spinal stenosis 24 and modic changes in the spine.…”

Section: Introductionmentioning

confidence: 99%

“… 18 , 19 , 20 , 21 , 22 These techniques can implement algorithms to isolate the tissue of interest 19 , 21 , 23 , 24 and then identify whether the tissue of interest is damaged, bulging, or degenerated. 18 , 19 , 20 , 21 , 22 Similarly, CNNs have been implemented to understand and interpret the severity of spinal stenosis 24 and modic changes in the spine. 23 Finally, Pfirrmann grading of the IVD has been automated using deep learning techniques including a CNN.…”

Section: Introductionmentioning

confidence: 99%

Automated magnetic resonance imaging‐based grading of the lumbar intervertebral disc and facet joints

Nikpasand,

Middendorf,

Ella

et al. 2024

JOR Spine

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BackgroundDegeneration of both intervertebral discs (IVDs) and facet joints in the lumbar spine has been associated with low back pain, but whether and how IVD/joint degeneration contributes to pain remains an open question. Joint degeneration can be identified by pairing T1 and T2 magnetic resonance imaging (MRI) with analysis techniques such as Pfirrmann grades (IVD degeneration) and Fujiwara scores (facet degeneration). However, these grades are subjective, prompting the need to develop an automated technique to enhance inter‐rater reliability. This study introduces an automated convolutional neural network (CNN) technique trained on clinical MRI images of IVD and facet joints obtained from public‐access Lumbar Spine MRI Dataset. The primary goal of the automated system is to classify health of lumbar discs and facet joints according to Pfirrmann and Fujiwara grading systems and to enhance inter‐rater reliability associated with these grading systems.MethodsPerformance of the CNN on both the Pfirrmann and Fujiwara scales was measured by comparing the percent agreement, Pearson's correlation and Fleiss kappa value for results from the classifier to the grades assigned by an expert grader.ResultsThe CNN demonstrates comparable performance to human graders for both Pfirrmann and Fujiwara grading systems, but with larger errors in Fujiwara grading. The CNN improves the reliability of the Pfirrmann system, aligning with previous findings for IVD assessment.ConclusionThe study highlights the potential of using deep learning in classifying the IVD and facet joint health, and due to the high variability in the Fujiwara scoring system, highlights the need for improved imaging and scoring techniques to evaluate facet joint health. All codes required to use the automatic grading routines described herein are available in the Data Repository for University of Minnesota (DRUM).

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Automatic recognition and calculation of softwood ray height based on tangential section images by using YOLOv5

Guo,

Wang,

Lin

et al. 2023

2023 China Automation Congress (CAC)

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In order to achieve rapid acquisition, identi cation and measurement of the average ray height of softwood based on tangential section photographs, a new method is proposed. Firstly, labels the digital image of the softwood tangential section with the 100 magni cation, that is, mark the rays and scales on the image, and establish the dataset; Secondly, the dataset is randomly divided into training set and validation set. YOLOv5s is used for model training to obtain the best target recognition model of rays and scale. The experimental results show that the model trained with YOLOv5s can achieve 93.5% accuracy, 95.6% recall and 96.7% average accuracy in the validation set; Thirdly, using the YOLOv5s trained model, a visual program for automatically calculating the ray height and obtaining the ray characteristics of softwood is designed, which lowered the threshold for wood identi cation workers to use such software.

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Deep Learning-Based Correlation Analysis between Spine Surgery Lumbar Facet Joint and Lumbar Disc Herniation Using Magnetic Resonance Images

Cited by 3 publications

References 21 publications

Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation

Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation

Automated magnetic resonance imaging‐based grading of the lumbar intervertebral disc and facet joints

Automatic recognition and calculation of softwood ray height based on tangential section images by using YOLOv5

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