DeU-Net 2.0: Enhanced deformable U-Net for 3D cardiac cine MRI segmentation

•A novel architecture DeU-Net 2.0, including Temporal Deformable Aggregation Module (TDAM), Enhanced Deformable Attention Network (EDAN) and Probabilistic Noise Correction Module (PNCM), was proposed for 3D cardiac cine MRI segmentation (average Dice score > 95% on the Extended ACDC dataset).•TDAM takes in consecutive MR slices as inputs to enhance the feature quality of the target slice by an offset prediction network and a temporal deformable convolutional layer.•EDAN exploits pyramidal architecture and high flexible deformable convolutional layers for accurate and robust segmentation.•In EDAN, we also propose Multi-Scale Attention Module (MSAM) to exploit multi-scale self-similarity and capture the useful correspondences at different scales.•PNCM considers two feature vectors as the normal feature and a random variable that is drawn from a standard Gaussian distribution by a full connection layer to alleviate the impact of noisy samples. [Display omitted] Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution, ambiguous borders and complicated shapes, existing methods suffer from the degradation of accuracy and robustness in cardiac MRI segmentation. In this paper, we propose an enhanced Deformable U-Net (DeU-Net) for 3D cardiac cine MRI segmentation, composed of three modules, namely Temporal Deformable Aggregation Module (TDAM), Enhanced Deformable Attention Network (EDAN), and Probabilistic Noise Correction Module (PNCM). TDAM first takes consecutive cardiac MR slices (including a target slice and its neighboring reference slices) as input, and extracts spatio-temporal information by an offset prediction network to generate fused features of the target slice. Then the fused features are also fed into EDAN that exploits several flexible deformable convolutional layers and generates clear borders of every segmentation map. A Multi-Scale Attention Module (MSAM) in EDAN is proposed to capture long range dependencies between features of different scales. Meanwhile, PNCM treats the fused features as a distribution to quantify uncertainty. Experimental results show that our DeU-Net achieves the state-of-the-art performance in terms of the commonly used evaluation metrics on the Extended ACDC dataset and competitive performance on other two datasets, validating the robustness and generalization of DeU-Net.