LSW-Net: Lightweight Deep Neural Network Based on Small-World properties for Spine MR Image Segmentation.

ABSTRACT

BACKGROUND: Segmenting spinal tissues from MR images is important for automatic image analysis. Deep neural network-based segmentation methods are efficient, yet have high computational costs. PURPOSE: To design a lightweight model based on small-world properties (LSW-Net) to segment spinal MR images, suitable for low-computing-power embedded devices. STUDY TYPE Retrospective. POPULATION A total of 386 subjects (2948 images) from two independent sources. Dataset I 214 subjects/779 images, all for disk degeneration screening, 147 had disk degeneration, 52 had herniated disc. Dataset II 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration. 70% images in each dataset for training, 20% for validation, and 10% for testing. FIELD STRENGTH/SEQUENCE T1- and T2-weighted turbo spin echo sequences at 3 T. ASSESSMENT Segmentation performance of LSW-Net was compared with four mainstream (including U-net and U-net++) and five lightweight models using five radiologists' manual segmentations (vertebrae, disks, spinal fluid) as reference standard. LSW-Net was also deployed on NVIDIA Jetson nano to compare the pixels number in segmented vertebrae and disks. STATISTICAL TESTS All models were evaluated with accuracy, precision, Dice similarity coefficient (DSC), and area under the receiver operating characteristic (AUC). Pixel numbers segmented by LSW-Net on the embedded device were compared with manual segmentation using paired t-tests, with P < 0.05 indicating significance. RESULTS: LSW-Net had 98.5% fewer parameters than U-net but achieved similar accuracy in both datasets (dataset I DSC 0.84 vs. 0.87, AUC 0.92 vs. 0.94; dataset II DSC 0.82 vs. 0.82, AUC 0.88 vs. 0.88). LSW-Net showed no significant differences in pixel numbers for vertebrae (dataset I 5893.49 vs. 5752.61, P = 0.21; dataset II 5073.42 vs. 5137.12, P = 0.56) and disks (dataset I 1513.07 vs. 1535.69, P = 0.42; dataset II 1049.74 vs. 1087.88, P = 0.24) segmentation on an embedded device compared to manual segmentation. DATA CONCLUSION: Proposed LSW-Net achieves high accuracy with fewer parameters than U-net and can be deployed on embedded device, facilitating wider application. EVIDENCE LEVEL 2. TECHNICAL EFFICACY 1.