MSRUNet: Multiscale Residual U-Net Network for Spine MRI Image Segmentation

Spinal medical images are essential for clinicians to diagnose spinal conditions. Computer segmentation can often be utilized to assist physicians in making a diagnosis, but traditional segmentation methods are time-consuming. Recently, deep learning has become more prevalent in medical imaging. Thi...

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Bibliographic Details
Published in2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA) pp. 285 - 290
Main Authors He, Junjie, Li, Kun, Qin, Juan, Ji, Yuehui, Xia, Dan, Fu, Xulong
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.07.2024
Subjects
ASPP
Computational modeling
Convolutional neural network
Deep learning
Image segmentation
Magnetic resonance imaging
Measurement
Medical diagnostic imaging
Multiscale residual
Performance analysis
Resilience
Spine
Spine MRI segmentation
Systems simulation
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DOI10.1109/CCSSTA62096.2024.10691809

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Summary:Spinal medical images are essential for clinicians to diagnose spinal conditions. Computer segmentation can often be utilized to assist physicians in making a diagnosis, but traditional segmentation methods are time-consuming. Recently, deep learning has become more prevalent in medical imaging. This paper introduces a high-performance automatic segmentation method for spinal MRI images using deep learning. The proposed method, called multiscale residual UNet (MSRUNet), enhances the classical U-Net architecture by incorporating residual double convolution and a customized ASPP block. These improvements allow the model to accurately focus on and segment various tissue and lesion regions in MRI images. This study assesses the model's performance on SpineSagT2Wdataset3 by measuring its recall, accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. Compared to other models, including U-Net, MSRUNet showed superior segmentation performance, demonstrating its effectiveness. This work advances automated spine MRI segmentation and promotes automated diagnosis and treatment of spinal disorders.
DOI:10.1109/CCSSTA62096.2024.10691809