MLP-Res-Unet: MLPs and Residual Blocks-based U-shaped Network Intervertebral Disc Segmentation of Multi-modal MR Spine Images

Title:MLP-Res-Unet: MLPs and Residual Blocks-based U-shaped Network Intervertebral Disc Segmentation of Multi-modal MR Spine Images

Volume: 20

Author(s): Hanqiang Liu, Sipei Lu*Feng Zhao

Affiliation:

• School of Computer Science, Shaanxi Normal University, Xi'an, China

Keywords: Deep learning, Intervertebral disc segmentation, Medical image segmentation, Multi-modal imaging, MRI, MR spine images.

Abstract:

Background: Intervertebral disc degeneration (IVD) is now the most prevalent disease in the world; thus, precise intervertebral disc segmentation is essential for the assessment and diagnosis of spinal diseases. Multi-modal magnetic resonance (MR) imaging is more multi-dimensional and thorough than unimodal imaging. However, manual segmentation of multi-modal MRI not only imposes a huge burden on physicians but also has a high error rate.

Objective: In this study, we propose a new method that can efficiently and accurately segment intervertebral discs from multi-modal MR spine images, providing a reproducible usage scheme for the diagnosis of spinal disorders.

Methods: We suggest a network structure called MLP-Res-Unet that reduces the amount of computational load and the number of parameters while maintaining performance. Our contribution is two-fold. First, a medical image segmentation network that fuses residual blocks and a multilayer perceptron (MLP) is proposed. Secondly, we design a new deep supervised method and pass the features extracted from the encoder to the decoder through the residual path to achieve a new full-scale residual connection.

Results: We evaluate the network on the MICCAI-2018 IVD dataset and obtain Dice similarity coefficient equal to 94.77 (%) and Jaccard coefficient equal to 84.74 (%), while we reduce the amount of parameters by a factor of 3.9 and computation by a factor of 2.4 compared to the IVD-Net.

Conclusion: Experiments show that MLP-Res-Unet improves segmentation performance and creates a simpler model structure while reducing the number of parameters and computation.

Cite this article as:

Liu Hanqiang, Lu Sipei*, Zhao Feng, MLP-Res-Unet: MLPs and Residual Blocks-based U-shaped Network Intervertebral Disc Segmentation of Multi-modal MR Spine Images, Current Medical Imaging 2024; 20 : e170423215841 . https://dx.doi.org/10.2174/1573405620666230417082855