A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation

Linchen Qian; Jiasong Chen; Linhai Ma; Timur Urakov; Liang Liang

doi:10.1117/12.3007064

2 April 2024 A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation

Linchen Qian, Jiasong Chen, Linhai Ma, Timur Urakov, Liang Liang

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292634 (2024) https://doi.org/10.1117/12.3007064
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

It is known that lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential factor on low back pain, a global health concern. Magnetic Resonance Imaging (MRI) plays a crucial role in detecting the morphologic changes and internal information of tissues. Thus, it is essential to develop accurate semantic segmentation of lumbar spine MRI images, which can assist doctors in recognizing the state of spinal degeneration in order to determine a suitable treatment. Recently, deep learning approaches have shown remarkable performance in medical image segmentation. However, the existing segmentation models often generate erroneously fragmented regions due to reasons related to texture similarity, image quality, or noises. In this work, we propose a novel neural network for the geometry reconstruction of lumbar spine from 2D mid-sagittal MRI images. We incorporated a UNet-style backbone with sequential geometry deformation modules to predict shape deformation on varying scales. We developed image feature extraction in a shape-specific manner to reject irrelevant information. We utilized self-attention mechanism to further process the extracted shape representation fused with image features and with a template as position embedding. We compared our model with some well-known models for image segmentation, including UNet++, Attention UNet, TransUNet, Swin-Unet, and UTNet. The results demonstrate that our model has the best performance, and its segmentation results are highly accurate and free of erroneous fragments. The source code is available at https://github.com/linchenq/SPIE2024-Geometry-Deformation-Lumbar-Spine-Segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Linchen Qian, Jiasong Chen, Linhai Ma, Timur Urakov, and Liang Liang "A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292634 (2 April 2024); https://doi.org/10.1117/12.3007064

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