Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec;58(6):1762-1776.
doi: 10.1002/jmri.28735. Epub 2023 Apr 29.

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

Siyuan He  1 , Qi Li  1   2 , Xianda Li  1 , Mengchao Zhang  3
Affiliations

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

Siyuan He et al. J Magn Reson Imaging. 2023 Dec.

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.

Keywords: lightweight deep neural network; medical image segmentation; small-world property; spine MR image.

PubMed Disclaimer

References

    1. Ajiboye LO, Alimi M, Gbadegesin SA, Oboirien M. Treatment outcome of quality of life and clinical symptoms in patients with symptomatic lumbar degenerative disc diseases: Which treatment modality is superior? Int Orthop 2019;43(4):875-881. https://doi.org/10.1007/s00264-018-4248-5.
    1. Hodgkinson T, Shen B, Diwan A, Hoyland JA, Richardson SM. Therapeutic potential of growth differentiation factors in the treatment of degenerative disc diseases. JOR Spine 2019;2(1):e1045. https://doi.org/10.1002/jsp2.1045.
    1. Su J, Li J, Zhang Y, Xia C, Tian Y. Selectivity or invariance: Boundary-aware salient object detection. Proceedings of the IEEE/CVF international conference on computer vision: Seoul: IEEE; 2019. p 3799-3808.
    1. Dolz J, Desrosiers C, Ayed IB. IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet. Granada: International workshop and challenge on computational methods and clinical applications for spine imaging: Springer; 2018. p 130-143. https://doi.org/10.1007/978-3-030-13736-6_11.
    1. Lenchik L, Heacock L, Weaver AA, et al. Automated segmentation of tissues using CT and MRI: A systematic review. Acad Radiol 2019;26(12):1695-1706. https://doi.org/10.1016/j.acra.2019.07.006.

MeSH terms

LinkOut - more resources