Abstract
Semantic segmentation has been popularly addressed using fully convolutional networks (FCNs) with impressive results if the training set is diverse and large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise samples are input and synergistically segmented in the network for capturing a rich context representation. To avoid overfitting introduced by appearance and shape changes in a small number of training samples, a fusion module is designed to provide proxy supervision for the network training process. Quantitative evaluation shows that the proposed method has a significant performance improvement on pathological liver segmentation.
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Acknowledgments
This work was supported by the China NSFC (11690011, 61661166011, 61721002, 81830053, U1811461) and the Key Area Research and Development Program of Guangdong Province, China (2018B010111001).
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Wang, R., Cao, S., Ma, K., Meng, D., Zheng, Y. (2019). Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_18
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DOI: https://doi.org/10.1007/978-3-030-32226-7_18
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