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

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).

Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation

“…Machine learning has shown great potential in segmenting medical images [7,35,36]. Existing works mostly handcrafted sophisticated neural network structures or frameworks to improve the cardiac MRI segmentation accuracy [4,7,9,13,24,32,35,36,41]. Recently, we have witnessed great success of neural architecture search (NAS), which can identify more accurate and efficient neural architectures than human-invented ones for medical image segmentation applications [8,20,27,33,38].…”