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Simultaneous multi-index quantification, segmentation, and uncertaintyestimation of liver tumors on multi-modality non-contrast magnetic resonanceimaging (NCMRI) are crucial for accurate diagnosis. However, existing methodslack an effective mechanism for multi-modality NCMRI fusion and accurateboundary information capture, making these tasks challenging. To address theseissues, this paper proposes a unified framework, namely edge-aware multi-tasknetwork (EaMtNet), to associate multi-index quantification, segmentation, anduncertainty of liver tumors on the multi-modality NCMRI. The EaMtNet employstwo parallel CNN encoders and the Sobel filters to extract local features andedge maps, respectively. The newly designed edge-aware feature aggregationmodule (EaFA) is used for feature fusion and selection, making the networkedge-aware by capturing long-range dependency between feature and edge maps.Multi-tasking leverages prediction discrepancy to estimate uncertainty andimprove segmentation and quantification performance. Extensive experiments areperformed on multi-modality NCMRI with 250 clinical subjects. The proposedmodel outperforms the state-of-the-art by a large margin, achieving a dicesimilarity coefficient of 90.01$\pm$1.23 and a mean absolute error of2.72$\pm$0.58 mm for MD. The results demonstrate the potential of EaMtNet as areliable clinical-aided tool for medical image analysis.

Edge-aware Multi-task Network for Integrating Quantification  Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality  Non-contrast MRI