陆军军医大学学报 (Nov 2023)
An intelligent cross-device segmentation algorithm for multi-disease cardiac MR images
Abstract
bjective To build a multi-disease cardiac magnetic resonance imaging(MRI) cross-device intelligent segmentation algorithm in order to improve the generality of the model in multi-disease conditions and different imaging devices. Methods Based on the M&Ms Challenge cardiac dataset (n=320) which was accepted by the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2020 as the research object, we proposed the hybrid loss function USO Loss to address the problem of poor generalization ability of existing segmentation models due to small sample data training, and improve the mainstream UNet, DeepLabV3+, and TransUNet algorithms in the multi-disease for different imaging device cardiac MRI data for segmentation. The experiments were divided into disease (n=320) and device groups (n=320), and internal and external data validation was performed. Results Dice similarity cofficient(DSC) and Hausdorff distance(HD) were used to evaluate the performance of model. The optimal segmentation results of DSC in disease group were 0.845 (dilated cardiomyopathy, n=20), 0.811(hypertrophic cardiomyopathy, n=20), 0.833(health volunteers, n=20)and 0.816(others, n=62), and of HD were 3.05, 2.53, 2.15 and 2.36 mm. The optimal segmentation results of DSC in device group were 0.830(Philips, n=20), 0.844(Siements, n=20), 0.843(Canon, n=20) and 0.815(General Electronics, n=50), and of HD were 1.96, 2.92, 1.67 and 2.08 mm. Compared with the models unused construction algorithm, models used USOLoss had improved results in all tests(P < 0.05). Conclusion The proposed USOLoss comprehensively improves the existing mainstream deep learning UNet, DeepLabV3+ and TransUNet network models performance and reduces the impact of different diseases and imaging devices on the segmentation algorithm.
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