Edge-aware Multi-task Network for Integrating Quantification
  Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality
  Non-contrast MRI
Xiaojiao Xiao Qinmin Hu Guanghui Wang
Simultaneous multi-index quantification, segmentation, and uncertainty
estimation of liver tumors on multi-modality non-contrast magnetic resonance
imaging (NCMRI) are crucial for accurate diagnosis. However, existing methods
lack an effective mechanism for multi-modality NCMRI fusion and accurate
boundary information capture, making these tasks challenging. To address these
issues, this paper proposes a unified framework, namely edge-aware multi-task
network (EaMtNet), to associate multi-index quantification, segmentation, and
uncertainty of liver tumors on the multi-modality NCMRI. The EaMtNet employs
two parallel CNN encoders and the Sobel filters to extract local features and
edge maps, respectively. The newly designed edge-aware feature aggregation
module (EaFA) is used for feature fusion and selection, making the network
edge-aware by capturing long-range dependency between feature and edge maps.
Multi-tasking leverages prediction discrepancy to estimate uncertainty and
improve segmentation and quantification performance. Extensive experiments are
performed on multi-modality NCMRI with 250 clinical subjects. The proposed
model outperforms the state-of-the-art by a large margin, achieving a dice
similarity coefficient of 90.01$\pm$1.23 and a mean absolute error of
2.72$\pm$0.58 mm for MD. The results demonstrate the potential of EaMtNet as a
reliable clinical-aided tool for medical image analysis.