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In this work, we propose MLP-Vnet, a token-based U-shaped multilayer linear perceptron-mixer (MLP-Mixer) network, incorporating a convolutional neural network for multi-structure segmentation on cardiac magnetic resonance imaging (MRI). The proposed MLP-Vnet is composed of an encoder and decoder. Taking an MRI scan as input, the semantic features are extracted by the encoder with one early convolutional block followed by four consecutive MLP-Mixer blocks. Then, the extracted features are passed to the decoder with mirrored architecture of the encoder to form a N-classes segmentation map. We evaluated our proposed network on the Automated Cardiac Diagnosis Challenge (ACDC) dataset. The performance of the network was assessed in terms of the volume- and surface-based similarities between the predicted contours and the manually delineated ground-truth contours, and computational efficiency. The volume-based similarities were measured by the Dice score coefficient (DSC), sensitivity, and precision. The surface-based similarities were measured by Hausdorff distance (HD), mean surface distance (MSD), and residual mean square distance (RMSD). The performance of the MLP-Vnet was compared with four state-of-the-art networks. The proposed network demonstrated statistically superior DSC and superior sensitivity or precision on all the three structures to the competing networks (p-value < 0.05): average DSC of 0.904, sensitivity of 0.908 and precision of 0.902 among all structures. The best surfaceased similarities were also demonstrated by the MLP-Vnet: average HD = 3.266 mm, MSD = 0.684 mm, and RMSD = 1.487 mm. Compared to the competing networks, the MLP-Vnet showed the shortest training time (7.32 hours) inference time per patient (3.12 seconds). The proposed MLP-Vnet is capable of using reasonable number of trainable parameters to solve the segmentation task on the cardiac MRI scans more quickly and accurately than the state-ofthe- art networks. This novel network could be a promising tool for accurate and efficient cardiac MRI segmentation to assist cardiac diagnosis and treatment decision making.
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