A spine segmentation method based on scene aware fusion network.

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.

Establishment of Central Cord Syndrome Model in C57BL/6J Mouse.

Animal models of central cord syndrome (CCS) could substantially benefit preclinical research. Identifiable anatomical pathways can give minimally invasive exposure approaches and reduce extra injury to experimental animals during operation, enabling the maintenance of consistent and stable anatomical morphology during experiments to minimize behavioral and histological differences between individuals to improve the reproducibility of experiments. In this study, the C6 level spinal cord was exposed using a spinal cord injury coaxial platform (SCICP) and combination with a minimally invasive technique. With the assistance of a vertebral stabilizator, we fixed the vertebrae and compressed the spinal cords of C57BL/6J mice with 5 g/mm2 and 10 g/mm2 weights with SCICP to induce different degrees of C6 spinal cord injury. In line with the previous description of CCS, the results reveal that the lesion in this model is concentrated in the gray matter around the central cord, enabling further research into CCS. Finally, histological results are provided as a reference for the readers.