Abstract
Recently, convolutional neural networks have shown their superior performance for biomedical image sequence segmentation. Most of the current state-of-the-art segmentation methods are designed for segmentation in static frames. However, misleading or missing features in static images may severely degrade segmentation performance. Effective incorporation of shape priors can alleviate this issue, which has been under-explored for deep models in previous attempts. In this paper, we explore the use of the continuity-based prior, either in temporal or spatial, with the aim of improving the robustness of the medical image sequence segmentation. Specifically, we firstly propose a nuclear norm minimization (NNM) based regularizer. However, nuclear norm tends to over-shrink the rank components, and all singular value are equally regularized. The singular values should be treated differently, as neighboring frames are more similar than distant frames. To rectify the weakness of NNM, we further propose to utilize the weighted nuclear norm minimization (WNNM), which achieves a better matrix rank approximation than NNMs and avoids over-regularization. To empirically investigate the effectiveness and robustness of the proposed sequential segmentation approach, we have performed extensive experiments on two different imaging modalities. The results demonstrate that our method can provide better robustness against missing features.
K. Xu and Z. Gao—Contributed equally to this work.
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Xu, K. et al. (2021). Batch Weighted Nuclear-Norm Minimization for Medical Image Sequence Segmentation. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_31
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