Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images. (July 2021)

• Record Type: Journal Article
• Authors: Cui, Hengfei; Yuwen, Chang; Jiang, Lei; Xia, Yong; Zhang, Yanning
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: Short-axis MRI -- Cardiac segmentation -- Attention U-Net -- Focal Tversky loss -- Multi-scale; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106142
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel U-Net based architecture guided by the multi-scale attention mechanism with input image pyramid and deep supervised output layers is developed for cardiac segmentation in short-axis MRI images. The Focal Tversky Loss function is incorporated into the attention mechanism gated U-Net, which can effectively tackle the problem of high imbalance between the background class and the target class. The multi-scale input image pyramid is improved in order to obtain better intermediate image features. Abstract: Background and Objective: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases. Methods: This paper proposes a new cardiac segmentation method in short-axis Magnetic Resonance Imaging (MRI) images, called attention U-Net architecture with input image pyramid and deep supervised output layers (AID), which can fully-automatically learn to pay attention to target structures of various sizes and shapes. During each training process, the model continues to learn how to emphasize the desired features and suppress irrelevant areas in the original images, effectively improving the accuracy of cardiac segmentation. At the same time, we introduce the Focal Tversky Loss (FTL), which can effectively solve the problem of high imbalance in the amount of data between the target class and the background class during cardiac image segmentation. In order to obtain a better representation of intermediate features, we add a multi-scale input pyramid to the attention network. Results: The proposed cardiac segmentation technique is tested on the public Left Ventricle Segmentation Challenge (LVSC) dataset, which is shown to achieve 0.75, 0.87 and 0.92 for Jaccard Index, Sensitivity and Specificity, respectively. Experimental results demonstrate that the proposed method is able to improve the segmentation accuracy compared with the standard U-Net, and achieves comparable performance to the most advanced fully-automated methods. Conclusions: Given its effectiveness and advantages, the proposed method can facilitate cardiac segmentation in short-axis MRI images in clinical practice.