Breast lesion segmentation result has a huge impact on the subsequent clinical analysis, and therefore it is of great importance for image-based diagnosis. In this paper, we propose a novel end-to-end network utilizing both spatial and temporal features for fully automated breast lesion segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Our network is based on a modified convolutional neural network and a recurrent neural network, and it is capable of unearthing rich spatio-temporal features. In our network, a multi-pathway structure and a fusion operator are introduced to acquire 3D information of different tissues, which is helpful for reducing false positive segmentation while boosting accuracy. Experimental results demonstrate that the proposed network produces a significantly more accurate result for lesion segmentation on our evaluation dataset, achieving 0.7588 dice coefficient and 0.7390 positive predictive value.
CITATION STYLE
Chen, M., Zheng, H., Lu, C., Tu, E., Yang, J., & Kasabov, N. (2018). A Spatio-Temporal Fully Convolutional Network for Breast Lesion Segmentation in DCE-MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11307 LNCS, pp. 358–368). Springer Verlag. https://doi.org/10.1007/978-3-030-04239-4_32
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