Abstract
The fully convolutional networks (FCNs) have achieved state-of-the-art performance in numerous medical image segmentation tasks. Most FCNs typically focus on fusing features in different levels to improve the learning ability to multi-scale features. In this paper, we explore an alternative direction to improve network performance by enhancing the encoding quality of high- and low-level features, so as to introduce two feature enhancement modules: (i) high-level feature enhancement module (HFE); (ii) low-level feature enhancement module (LFE). HFE utilizes attention mechanism to selectively aggregate the optimal feature information in high- and low-levels, enhancing the ability of high-level features to reconstruct accurate details. LFE aims to use global semantic information of high-level features to adaptively guide feature learning of bottom networks, so as to enhance the semantic consistency of high- and low-level features. We integrate HFE and LFE into a typical encoder-decoder network, and propose a novel medical image segmentation framework (HLF-Net). On two challenging datasets of skin lesion segmentation and spleen segmentation, we prove that the proposed modules and network can improve the performance considerably.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
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
Wang, H., Gu, R., Li, Z.: Automated segmentation of intervertebral disc using fully dilated separable deep neural networks. In: Zheng, G., Belavy, D., Cai, Y., Li, S. (eds.) CSI 2018. LNCS, vol. 11397, pp. 66–76. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13736-6_6
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16
Zhang, Z., Zhang, X., Peng, C., Xue, X., Sun, J.: ExFuse: enhancing feature fusion for semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 273–288. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_17
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421–429. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_48
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Wang, F., et al.: Residual attention network for image classification. In: CVPR, pp. 3156–3164 (2017)
Wu, Y., He, K.: Group normalization. arXiv preprint arXiv:1803.08494 (2018)
Bi, L., Kim, J., Ahn, E., Feng, D.: Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint arXiv:1703.04197 (2017)
Berseth, M.: ISIC 2017-skin lesion analysis towards melanoma detection. arXiv preprint arXiv:1703.00523 (2017)
Yuan, Y.: Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv:1803.08494 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Wang, G., Xu, Z., Lei, W., Zhang, S. (2019). High- and Low-Level Feature Enhancement for Medical Image Segmentation. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)
