Abstract
Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. However, this task is formidable because of the enormous variability in glandular appearance and the difficulty in distinguishing between glandular and non-glandular histological structures. To address this challenge, a novel neural network is proposed to effectively extract benign or malignant colon glands from histology images. Our network has the following innovations: (1) Under the same resolution space, multi-receptive field pyramid features are captured from dense multi-rate dilated convolution architecture with sounder dilated rate setting for accurate gland segmentation. (2) Furthermore, the extracted features are down-sampled by B-spline algorithm to provide different resolution information for the network. (3) Specifically, we embed spatial-channel soft attention modules before each deconvolution operation in the decoder phase, which can better reintegrate features and support clear semantic similarity for network. All these unique operations boost the performance of our method on gland segmentation task. We achieve state-of-the-art performance on the publicly available Warwick-QU dataset.
This project was supported by grants from the Nature Science Foundation of China (Nos. NSFC81401539 and NSFC31271056) and the projects in the Shenzhen Medical Engineering Laboratory For Human Auditory-equilibrium Function.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Moridikia, A.: Potential candidates for diagnosis and treatment of colorectal cancer. J. Cell. Physiol. 233(2), 901–913 (2018)
Kainz, P.: Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. PeerJ 5, e3874 (2017)
Xu, Y.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinform. 18(1), 281 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Chen, H.: DCAN: deep contour-aware networks for accurate gland segmentation. In: The IEEE conference on Computer Vision and Pattern Recognition, pp. 2487–2496 (2016)
Sirinukunwattana, K.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489–502 (2017)
Xu, Y., Li, Y., Liu, M., Wang, Y., Lai, M., Chang, E.I.-C.: Gland instance segmentation by deep multichannel side supervision. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 496–504. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_57
Raza, S.E.A., Cheung, L., Epstein, D., Pelengaris, S., Khan, M., Rajpoot, Nasir M.: MIMONet: gland segmentation using multi-input-multi-output convolutional neural network. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 698–706. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_61
Chen, L.C.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Yang, M.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684–3692 (2016)
Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., Jia, J.: PSANet: point-wise spatial attention network for scene parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 270–286. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_17
He, K.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: The IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, S. et al. (2019). DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-32689-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32688-3
Online ISBN: 978-3-030-32689-0
eBook Packages: Computer ScienceComputer Science (R0)
