Abstract
Segmenting pancreas from CT images is of great significance for clinical diagnosis and research. Traditional encoder-decoder networks, which are widely used in medical image segmentation, may fail to address low tissue contrast and large variability of pancreas shape and size due to underutilization of multi-level features and context information. To address these problems, this paper proposes a novel feature enhanced and context inference network (FECI-Net) for pancreas segmentation. Specifically, features are enhanced by imposing saliency region constraints to mine complementary regions and details between multi-level features; Gated Recurrent Unit convolution (ConvGRU) is introduced in the decoder to fully contact the context aimed to capture task-relevant fine features. By comparing experimental evaluations on the NIH-TCIA dataset, our method improves IOU and Dice by 5.5% and 4.1% respectively compared to the baseline, which outperforms current state-of-the-art medical image segmentation methods.
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References
Li, Z., Fan, J., Ren, Y., Tang, L.: A novel feature extraction approach based on neighborhood rough set and PCA for migraine rs-fMRI. J. Intell. Fuzzy Syst. 38(6), 1–11 (2020)
Patil, D.D., Deore, S.G.: Medical image segmentation: a review. Int. J. Comput. Sci. Mob. Comput. 2(1), 22–27 (2013)
Liu, Y., Chen, S.: Review of medical image segmentation method. Electr. Sci. Technol. 30(8), 169–172 (2017)
Popilock, R., Sandrasagaren, K., Harris, L., et al.: CT artifact recognition for the nuclear technologist. J. Nucl. Med. Technol. 36(2), 79–81 (2008)
Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G., Chen, J.: Detection of malicious code variants based on deep learning. IEEE Trans. Industr. Inf. 14(7), 3187–3196 (2018)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII, pp. 833–851. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Zhao, H.S., Shi, J.P., Qi, X.J., Wang, X.G., Jia, J.Y.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 6230–6239. Honolulu, HI, USA (2017)
Kumar, N., Hoffmann, N., Oelschlägel, M., Koch, E., Kirsch, M., Gumhold, S.: Structural similarity based anatomical and functional brain imaging fusion. In: Zhu, D., et al. (eds.) MBIA/MFCA -2019. LNCS, vol. 11846, pp. 121–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33226-6_14
Vo, X.-T., Tran, T.-D., Nguyen, D.-L., Jo, K.-H.: Stair-step feature pyramid networks for object detection. In: Jeong, H., Sumi, K. (eds.) IW-FCV 2021. CCIS, vol. 1405, pp. 168–175. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-81638-4_13
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Huang, H., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059. Barcelona, Spain (2020)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Kaku, A., Hegde, C.V., Huang, J., et al.: DARTS: DenseUnet-based automatic rapid tool for brain segmentation. arXiv preprint arXiv:1911.05567 (2019)
Yu, Q., et al.: Crosslink-Net: double-branch encoder segmentation network via fusing vertical and horizontal convolutions. arXiv preprint arXiv:2107.11517 (2021)
Gu, Z., et al.: CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Anderson, P., et al.: Bottom-Up and top-down attention for image captioning and visual question answering. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6077–6086. Salt Lake City, UT, USA (2018)
Bahdanau, D., et al.: Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2015)
Liang, L., Cao, J., Li, X., You, J.: Improvement of residual attention network for image classification. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds.) IScIDE 2019. LNCS, vol. 11935, pp. 529–539. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36189-1_44
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Oktay, O., Schlemper, J., Folgoc, L., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Sinha, A., Dolz, J.: Multi-scale self-guided attention for medical image segmentation. IEEE J. Biomed. Health Inform. 25(1), 121–130 (2021)
Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26
Wang, Y., Ni, Z., Song, S., et al.: Revisiting locally supervised learning: an alternative to end-to-end training. In: International Conference on Learning Representations (2021)
Ballas, Nicolas, et al.: Delving deeper into convolutional networks for learning video representations. arXiv preprint arXiv:1511.06432 (2015)
Roth, H., Farag, A., Turkbey, E.B., Lu, L., Liu, R.M.: Data from pancreas-CT. Cancer Imag. Arch. (2016). https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF009, the State Key Research Development Program of China under Grant 2017YFC0804406, the Taishan Scholars Program of Shandong Province under Grant ts20190936, and the Shandong University of Science and Technology Research Fund under Grant 2015TDJH102.
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Lou, Zh., Fan, Jc., Ren, Yd., Tang, Ly. (2022). Feature Enhanced and Context Inference Network for Pancreas Segmentation. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_18
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DOI: https://doi.org/10.1007/978-981-19-1253-5_18
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