Abstract
Magnetic resonance imaging (MRI) has been widely applied to the medical imaging diagnosis of various human body systems. Deep network-based medical image segmentation techniques for MRI can help patients receive more accurate and effective diagnoses. However, multiple consecutive two-dimensional MRIs have sequence information in reality. Different organs may appear specifically in a sequence of MRI data for the body part. Therefore, MRI sequence information is the key to improving the segmentation effect in deep network architecture design. In this paper, we propose the SIA-Unet, an improved Unet network that incorporates MRI sequence information. SIA-Unet also has an attention mechanism to filter the feature map’s spatial data to extract valuable data. Extensive experiments on the UW-Madison dataset have been conducted to evaluate the performance of SIA-Unet. Experimental results have shown that with a coherent end-to-end training pipeline, SIA-Unet significantly outperforms other baselines. Our implementation is available at https://github.com/min121101/SIA-Unet.
Supported by the National Natural Science Foundation of China (62006044).).
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
Notes
- 1.
\(\sigma _{1}(x)=1 / (1+e^{-x})\).
- 2.
\(\sigma _{2}(x)=\max (0, x)\).
References
Bai, W., et al.: Human-level cmr image analysis with deep fully convolutional networks (2017)
Gong, J., Kang, W., Zhu, J., Xu, J.: Ct and mr imaging of gastrointestinal stromal tumor of stomach: a pictorial review. Quant. Imaging Med. Surg. 2(4), 274 (2012)
Guan, S., Khan, A.A., Sikdar, S., Chitnis, P.V.: Fully dense unet for 2-d sparse photoacoustic tomography artifact removal. IEEE J. Biomed. Health Inform. 24(2), 568–576 (2019)
Horie, Y., et al.: Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest. Endosc. 89(1), 25–32 (2019)
Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21–45 (2019)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Liao, F., Liang, M., Li, Z., Hu, X., Song, S.: Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE transactions on neural networks and learning systems 30(11), 3484–3495 (2019)
Liu, K., Ye, R., Zhongzhu, L., Ye, R.: Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques. PLoS ONE 17(3), e0265633 (2022)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Motohara, T., Semelka, R.: Mri in staging of gastric cancer. Abdominal Radiol. 27(4), 376 (2002)
Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
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
Roth, H.R., Lu, L., Lay, N., Harrison, A.P., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med. Image Anal. 45, 94–107 (2018)
Roth, H.R., et al.: Hierarchical 3d fully convolutional networks for multi-organ segmentation. arXiv preprint arXiv:1704.06382 (2017)
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘Squeeze & Excitation’ in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., 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
Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3d fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379–387. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_44
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Urban, G., et al.: Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155(4), 1069–1078 (2018)
Wang, D., et al.: Afp-net: Realtime anchor-free polyp detection in colonoscopy. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 636–643. IEEE (2019)
Wessling, J., Schreyer, A., Grenacher, L., Juchems, M., Ringe, K.: Incidental and" leave me alone" findings in the gi tract-part 1: Intestinal lumen and intestinal wall. Der Radiologe (2022)
Ye, R., Guo, Y., Shuai, X., Ye, R., Jiang, S., Jiang, H.: Licam: Long-tailed instance segmentation with real-time classification accuracy monitoring. J. Circ. Syst. Comput., 2350032 (2022)
Ye, R., Ye, R., Zheng, S.: Machine learning guides the solution of blocks relocation problem in container terminals. Trans. Res. Record p. 03611981221117157 (2022)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ye, R., Wang, R., Guo, Y., Chen, L. (2022). SIA-Unet: A Unet with Sequence Information for Gastrointestinal Tract Segmentation. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-20862-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20861-4
Online ISBN: 978-3-031-20862-1
eBook Packages: Computer ScienceComputer Science (R0)