Abstract
This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localization) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Okada, T., et al.: Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 86–93. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75757-3_11
Chu, C., et al.: Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 165–172. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_21
Wolz, R., Chu, C., Misawa, K., et al.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE TMI 32(9), 1723–1730 (2013)
Karasawa, K., et al.: Structure specific atlas generation and its application to pancreas segmentation from contrasted abdominal CT volumes. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Zhang, S., Cai, W., Metaxas, D. (eds.) MCV 2015. LNCS, vol. 9601, pp. 47–56. Springer, Cham (2016). doi:10.1007/978-3-319-42016-5_5
Tong, T., Wolz, R., Wang, Z., et al.: Discriminative dictionary learning for abdominal multi-organ segmentation. Med. Image Anal. 23(1), 92–104 (2015)
Saito, A., Nawano, S., Shimizu, A.: Joint optimization of segmentation and shape prior from level set-based statistical shape model, and its application to the automated segmentation of abdominal organs. Med. Image Anal. 28, 46–65 (2016)
Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_52
Oda, M., et al.: Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 556–563. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_64
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_49
Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: 22nd ACM International Conference On Multimedia, pp. 675–678. ACM (2014)
Criminisi, A., Robertson, D., Konukoglu, E., et al.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Med. Image Anal. 17, 1293–1303 (2013)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23(11), 1222–1239 (2001)
Acknowledgments
Parts of this research were supported by the MEXT/JSPS KAKENHI Grant Numbers 25242047, 26108006, 17H00867, the JSPS Bilateral International Collaboration Grants, and the JST ACT-I (JPMJPR16U9).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Oda, M. et al. (2017). 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)