Automatic computed tomography image segmentation method for liver tumor based on a modified tokenized multilayer perceptron and attention mechanism.

Abstract

Background: The automatic medical image segmentation of liver and tumor plays a pivotal role in the clinical diagnosis of liver diseases. A number of effective methods based on deep neural networks, including convolutional neural networks (CNNs) and vision transformer (ViT) have been developed. However, these networks primarily focus on enhancing segmentation accuracy while often overlooking the segmentation speed, which is vital for rapid diagnosis in clinical settings. Therefore, we aimed to develop an automatic computed tomography (CT) image segmentation method for liver tumors that reduces inference time while maintaining accuracy, as rigorously validated through experimental studies.

Methods: We developed a U-shaped network enhanced by a multiscale attention module and attention gates, aimed at efficient CT image segmentation of liver tumors. In this network, a modified tokenized multilayer perceptron (MLP) block is first leveraged to reduce the feature dimensions and facilitate information interaction between adjacent patches so that the network can learn the key features of tumors with less computational complexity. Second, attention gates are added into the skip connections between the encoder and decoder, emphasizing feature expression in relevant regions and enabling the network to focus more on liver tumor features. Finally, a multiscale attention mechanism autonomously adjusts weights for each scale, allowing the network to adapt effectively to varying sizes of liver tumors. Our methodology was validated via the Liver Tumor Segmentation 2017 (LiTS17) public dataset. The data from this database are from seven global clinical sites. All data are anonymized, and the images have been prescreened to ensure the absence of personal identifiers. Standard metrics were used to evaluate the performance of the model.

Results: The 21 cases were included for testing. The proposed network attained a Dice score of 0.713 [95% confidence interval (CI): 0.592-0.834], a volumetric overlap error of 0.39 (95% CI: 0.17-0.61), a relative volume difference score of 0.19 (95% CI: -0.37 to 0.31), an average symmetric surface distance of 2.04 mm (95% CI: 0.89-4.19), a maximum surface distance of 9.42 mm (95% CI: 6.97-19.87), and an inference time of 26 ms on average for liver tumor segmentation.

Conclusions: The proposed network demonstrated efficient liver tumor segmentation performance with less inference time. Our findings contribute to the application of neural networks in rapid clinical diagnosis and treatment.