Abstract
Although a large number of 2D convolutional neural networks have been reported and used for medical image segmentation, most of them still face the following two problems. First, these networks employ 2D convolution to achieve 3D organ segmentation, which ignores the relationship between different slices. Second, these networks depend on U-shape networks that employ skip-connection to fusion low-level and high-level features, which ignores the semantic gap between them. To address these two issues, we propose a residual inter-slices feature learning method employed by a 2D network that can achieve high-efficient 3D organ segmentation. First, we present an image pre-processing approach named residual inter-slice feature enhancement. We use the residual image including 3D information instead of the original medical slices, which is more efficient and effective than using a 3D network. Second, we present a double-focus skip-connection(DFSC) that is used for replacing the vanilla skip-connection in an encoder-decoder network. Because the DFSC is able to narrow the semantic gap between low-level and high-level features, it achieves better feature fusion. Experiments show that the proposed method is useful for improving feature representation ability of networks and achieving higher organ segmentation accuracy with lower cost.
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Acknowledgements
This work was supported in part by Key Research and Development Program of Shaanxi (Program No. 2022GY-436, 2021ZDLGY08-07), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2021JC-47).
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Zhang, J. et al. (2023). Residual Inter-slice Feature Learning for 3D Organ Segmentation. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14359. Springer, Cham. https://doi.org/10.1007/978-3-031-46317-4_12
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DOI: https://doi.org/10.1007/978-3-031-46317-4_12
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