<i>Retracted on March 1, 2022</i>
            : Vertebrae Segmentation from X-ray Images Using Convolutional Neural Network

Kuok, Chan-Pang; Fu, Min-Jun; Lin, Chih-Hao; Horng, Ming-Huwi; Sun, Yung-Nien

doi:10.1145/3292425.3293463

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…(Figure 6.) Kuok et al 19 used a three-step system that included spine RoI detection, vertebrae RoI detection and vertebrae segmentation on a.p. whole-body images.…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

Konya

Natarajan²,

Allouch

et al. 2021

Journal of Craniovertebral Junction and Spine

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Purpose: This study investigated the segmentation metrics of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays to test the generalization ability and robustness which are the basis of clinical decision support algorithms. Methods: Instance segmentation networks were compared to semantic segmentation networks based on different metrics. The study cohort comprised diseased spines and postoperative images with metallic implants. Results: However, the pixel accuracies and intersection over union are similarly high for the best performing instance and semantic segmentation models; the observed vertebral recognition rates of the instance segmentation models statistically significantly outperform the semantic models' recognition rates. Conclusion: The results of the instance segmentation models on lumbar spine X-ray perform superior to semantic segmentation models in the recognition rates even by images of severe diseased spines by allowing the segmentation of overlapping vertebrae, in contrary to the semantic models where such differentiation cannot be performed due to the fused binary mask of the overlapping instances. These models can be incorporated into further clinical decision support pipelines.

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“…(Figure 6.) Kuok et al 19 used a three-step system that included spine RoI detection, vertebrae RoI detection and vertebrae segmentation on a.p. whole-body images.…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

Konya

Natarajan²,

Allouch

et al. 2021

Journal of Craniovertebral Junction and Spine

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“…The L2 loss function was employed, and the cross validation method was used in the experiments. The dice similarity coefficient (DSC) of the X-ray images was 94.1% in the experiment [34].…”

Section: Deep Learning For Semantic Segmentationmentioning

confidence: 94%

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

Trinh¹,

Shao²,

Hsieh³

et al. 2022

Preprint

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A common spinal condition, spondylolisthesis is the presence of a relative back or forth displacement between the upper and lower vertebra due to one vertebra being oriented away from the smooth curvature of a normal spine. Aging-related illnesses such as degenerative spondylolisthesis are especially burdensome on social welfare and health-care systems in an aging society, especially radiologists and clinical physicians. Therefore, we proposed a computer aided diagnosis algorithm, named LumbarNet, for vertebral slippage detection on clinical X-ray images. Collaborating with i) a P-grade, ii) a piecewise slope detection scheme, and iii) a dynamic shift detection routine, LumbarNet was thus specialized for analyzing complex structural patterns in lumbar spine X-ray images and outcompeted other U-Net based methods. Extensive experiments on lumbar spine X-ray images in standard clinical practices showed that LumbarNet achieved a mean intersection over union value of 0.88 in vertebral region detection and an accuracy of 88.83% in vertebral slippage detection.

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“…Kuok et al [24] proposed a hybrid approach using image processing for the detection of the vertebrae and CNN in the segmentation task of the vertebrae. They used a private dataset from the National Cheng Kung University Hospital in Taiwan for 60 X-ray images.…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach for Lumbar Spine Disc State Classification

Al-Kubaisi

Khamiss

2021

Electronics

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Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.

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Retracted on March 1, 2022 : Vertebrae Segmentation from X-ray Images Using Convolutional Neural Network

Cited by 10 publications

References 12 publications

Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

A Transfer Learning Approach for Lumbar Spine Disc State Classification

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