Using minimally invasive methods to model spinal cord injury (SCI) can minimize behavioral and histological differences between experimental animals, thereby improving the reproducibility of the experiments.These methods need two requirements to be fulfilled: clarity of the surgical anatomical pathway and simplicity and convenience of the laboratory device. Crucially for the operator, a clear anatomical pathway provides minimally invasive exposure, which avoids additional damage to the experimental animal during the surgical procedures and allows the animal to maintain a consistent and stable anatomical morphology during the experiment.In this study, the use of a novel integrated platform called the SCI coaxial platform for spinal cord injury in small animals to expose the T9 level spinal cord in a minimally invasive way and stabilize and immobilize the vertebra of mice using a vertebral stabilizer is researched, and, finally, a coaxial gravity impactor is used to contuse the spinal cord of mice to approach different degrees of T9 spinal cord injury. Finally, histological results are provided as a reference for the readers.

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.