This article discusses the development of a deep learning network called SAFNet for the standardized interpretation and automated classification of lumbar spine MRI. The network extracts low-level, mid-level, and high-level features from the images and processes them using different techniques to obtain more accurate segmentation results. The results of the study show that SAFNet achieved a Dice similarity coefficient (DSC) of 80.32% for segmenting vertebral structures, outperforming existing methods. The researchers conclude that SAFNet is a highly accurate and robust network that has the potential to improve radiological diagnosis accuracy in the field of spinal and lumbar diseases. The keywords associated with this study are 3D segmentation, deep learning, MRI, and spine
Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : best recognised spinal centre in London
Published article
CONCLUSIONS: This research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy.
Spine Lumbar Spinal Stenosis Expert. Best Spinal Surgeon UK
Abstract Background: Intervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to,
Abstract
Background: Intervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to subjective errors caused by diagnosticians or differences in diagnostic criteria for multi-center studies in different hospitals, and inefficient diagnosis. These factors necessitate the standardized interpretation and automated classification of lumbar spine MRI to achieve objective consistency. In this research, a deep learning network based on SAFNet is proposed to solve the above challenges.
Methods: In this research, low-level features, mid-level features, and high-level features of spine MRI are extracted. ASPP is used to process the high-level features. The multi-scale feature fusion method is used to increase the scene perception ability of the low-level features and mid-level features. The high-level features are further processed using global adaptive pooling and Sigmoid function to obtain new high-level features. The processed high-level features are then point-multiplied with the mid-level features and low-level features to obtain new high-level features. The new high-level features, low-level features, and mid-level features are all sampled to the same size and concatenated in the channel dimension to output the final result.
Results: The DSC of SAFNet for segmenting 17 vertebral structures among 5 folds are 79.46 ± 4.63%, 78.82 ± 7.97%, 81.32 ± 3.45%, 80.56 ± 5.47%, and 80.83 ± 3.48%, with an average DSC of 80.32 ± 5.00%. The average DSC was 80.32 ± 5.00%. Compared to existing methods, our SAFNet provides better segmentation results and has important implications for the diagnosis of spinal and lumbar diseases.
Conclusions: This research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy.
Keywords: 3D segmentation; Deep learning; MRI; Spine.
The London Spine Unit : best recognised spinal centre in London
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A spine segmentation method based on scene aware fusion network
