RCLM-net: A rich-club properties based lightweight deep neural network for multimodal sequence spine MR images segmentation

The diagnosis of spinal diseases relies on the comprehensive assessment of multimodal sequence MR images. As a prior foundation for intelligent aid diagnosis, it is important to design an efficient MR image segmentation model. However, previous models for multimodal images tend to have a large number of parameters, which is not suitable for low-computing-power application scenarios, especially for primary medical sites. To address the above problem, this study constructs a lightweight neural network model for multimodal sequence spine MR images segmentation, named RCLW-Net (Rich-Club Lightweight Net), which is inspired by the “Rich-Club” properties of brain functional networks. Specifically, our network effectively reduced the number of model parameters by constructing a compact network structure that satisfies the Rich-Club properties and combining the Recurrent residual Convolution as the base convolution module. Verified by the five-fold cross validation of the three-modal sequence MR spine images containing 200 patients' data, proposed RCLW-Net has only 11 % of the parameter scale of the classical Unet++, but the segmentation performance is better. Moreover, proposed RCLW-Net can be successfully deployed on a lightweight embedded device Jetson Nano B01. The low number of parameters and the high performance of the RCLW-Net show the potential of the application in low-computing power scenarios.