Super U-Net: A modularized generalizable architecture

Objective: To develop and validate a novel convolutional neural network (CNN) termed Super U-Net for medical image segmentation. Methods: Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.e., fundus images, endoscopic images, dermoscopic images). We also trained and tested the traditional U-Net architecture, seven U-Net variants, and two non-U-Net segmentation architectures. K-fold cross-validation was used to evaluate performance. The performance metrics included Dice similarity coefficient (DSC), accuracy, positive predictive value (PPV), and sensitivity. Results: Super U-Net achieved average DSCs of 0.808 +/- 0.0210, 0.752 +/- 0.019, 0.804 +/- 0.239, and 0.877 +/- 0.135 for segmenting retinal vessels, pediatric retinal vessels, GI polyps, and skin lesions, respectively. The Super U-net consistently outperformed U-Net, seven U-Net variants, and two non-U-Net segmentation architectures (p < 0.05). Conclusion: Dynamic receptive fields and fusion upsampling can significantly improve image segmentation performance. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study developed and validated a novel convolutional neural network called Super U-Net for medical image segmentation. The Super U-Net integrates dynamic receptive field module and fusion upsampling module to improve image segmentation performance.