DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation

• URL: http://arxiv.org/abs/2007.06341v1
• Date: Mon, 13 Jul 2020 12:19:03 GMT
• Title: DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation
• Authors: Shunjie Dong, Jinlong Zhao, Maojun Zhang, Zhengxue Shi, Jianing Deng, Yiyu Shi, Mei Tian, Cheng Zhuo
• Abstract summary: We propose a novel Deformable U-Net to fully exploit temporal information from 3D cardiac MRI video. To aggregate meaningful features, we devise the DGPA network by employing deformable attention U-Net. Experimental results show that our DeU-Net achieves the state-of-the-art performance on commonly used evaluation metrics.
• Score: 16.85475295093217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution and ambiguous border (e.g., right ventricular endocardium), existing methods suffer from the degradation of accuracy and robustness in 3D cardiac MRI video segmentation. In this paper, we propose a novel Deformable U-Net (DeU-Net) to fully exploit spatio-temporal information from 3D cardiac MRI video, including a Temporal Deformable Aggregation Module (TDAM) and a Deformable Global Position Attention (DGPA) network. First, the TDAM takes a cardiac MRI video clip as input with temporal information extracted by an offset prediction network. Then we fuse extracted temporal information via a temporal aggregation deformable convolution to produce fused feature maps. Furthermore, to aggregate meaningful features, we devise the DGPA network by employing deformable attention U-Net, which can encode a wider range of multi-dimensional contextual information into global and local features. Experimental results show that our DeU-Net achieves the state-of-the-art performance on commonly used evaluation metrics, especially for cardiac marginal information (ASSD and HD).

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