PDF(Pubmed)
Abstract：
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.
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.
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.
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.
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.
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.
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.
摘要：
背景：椎间盘突出症，退行性腰椎管狭窄症,和其他腰椎疾病可以发生在大多数年龄组。MRI检查以其良好的软组织图像分辨率成为腰椎病变最常用的检测方法。然而,诊断准确性高度依赖于诊断医生的经验，导致诊断医生的主观错误或不同医院多中心研究的诊断标准差异，低效的诊断。这些因素需要腰椎MRI的标准化解释和自动分类以实现客观一致性。在这项研究中,提出了一种基于SAFNet的深度学习网络来解决上述挑战。
方法：在这项研究中，低级功能，中级功能，并提取脊柱MRI的高级特征。ASPP用于处理高级特征。采用多尺度特征融合方法,提高了底层特征和中层特征的场景感知能力。使用全局自适应池化和Sigmoid函数进一步处理高级特征以获得新的高级特征。然后将经处理的高级特征与中级特征和低级特征点相乘以获得新的高级特征。新的高级功能，低级功能，和中级特征都被采样到相同的大小，并在通道维度中级联以输出最终结果。
结果：SAFNet对5折17节椎体结构的DSC为79.46±4.63%，78.82±7.97%，81.32±3.45%，80.56±5.47%，80.83±3.48%，平均DSC为80.32±5.00%。平均DSC为80.32±5.00%。与现有方法相比，我们的SAFNet提供了更好的分割结果,对脊柱和腰椎疾病的诊断具有重要意义.
结论：这项研究提出了SAFNet，一个高度准确和强大的脊柱分割深度学习网络，能够为诊断目的提供有效的解剖分割。结果证明了该方法的有效性及其提高放射学诊断准确性的潜力。
方法：在这项研究中，低级功能，中级功能，并提取脊柱MRI的高级特征。ASPP用于处理高级特征。采用多尺度特征融合方法,提高了底层特征和中层特征的场景感知能力。使用全局自适应池化和Sigmoid函数进一步处理高级特征以获得新的高级特征。然后将经处理的高级特征与中级特征和低级特征点相乘以获得新的高级特征。新的高级功能，低级功能，和中级特征都被采样到相同的大小，并在通道维度中级联以输出最终结果。
结果：SAFNet对5折17节椎体结构的DSC为79.46±4.63%，78.82±7.97%，81.32±3.45%，80.56±5.47%，80.83±3.48%，平均DSC为80.32±5.00%。平均DSC为80.32±5.00%。与现有方法相比，我们的SAFNet提供了更好的分割结果,对脊柱和腰椎疾病的诊断具有重要意义.
结论：这项研究提出了SAFNet，一个高度准确和强大的脊柱分割深度学习网络，能够为诊断目的提供有效的解剖分割。结果证明了该方法的有效性及其提高放射学诊断准确性的潜力。
