Abstract
Ultrasound image segmentation plays an essential role in automatic disease diagnosis. However, to achieve precise ultrasound segmentation is still a challenge caused by the ambiguous lesion boundary and imaging artifacts such as speckles and shadowing noise. Considering that the pixels with high uncertainty generally distributing in the boundary regions of prediction maps, are likely to overlap with the confused regions of ultrasound, we proposed an uncertainty-aware cascade network. Our network uses the confidence map to evaluate the uncertainty of each pixel to enhance the segmentation of ambiguous boundary. On the one hand, the confidence map fuses with the ultrasound features and predicted mask using the adaptive fusion module (AFM) which enriches the context features from different modalities. In addition, the uncertainty attention module (UAM) is proposed based on the confidence map. This module focuses on the influential features with cross attention constrained by the uncertainty of pixels which can extract the localized features of confused ultrasound regions. On the other hand, the recurrent edge correction module (RECM) further improves the segmentation of ambiguous boundary. This module increases the weights of confident features neighboring the uncertainty boundaries in order to refine the predictions of edge pixels with low confidence. We evaluated the proposed method on three public ultrasound datasets and the segmentation results show that our method achieved higher Dice scores and lower Hausdorff distance with more precise boundary details compared with state-of-the-art methods.
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Acknowledgements
The authors acknowledge supports from National Nature Science Foundation of China grants (U20A20389, 61901214, 82027807), China Postdoctoral Science Foundation (2021T140322, 2020M671484), Jiangsu Planned Projects for Postdoctoral Research Funds (2020Z024).
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Xie, Y., Liao, H., Zhang, D., Chen, F. (2022). Uncertainty-aware Cascade Network for Ultrasound Image Segmentation with Ambiguous Boundary. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_26
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