Abstract
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis. For anatomically high-variability abdominal organs such as the pancreas, previous segmentation works report low accuracies when comparing to organs like the heart or liver. In this paper, a fully-automated bottom-up method is presented for pancreas segmentation, using abdominal computed tomography (CT) scans. The method is based on a hierarchical two-tiered information propagation by classifying image patches. It labels superpixels as pancreas or not via pooling patch-level confidences on 2D CT slices over-segmented by the Simple Linear Iterative Clustering approach. A supervised random forest (RF) classifier is trained on the patch level and a two-level cascade of RFs is applied at the superpixel level, coupled with multi-channel feature extraction, respectively. On six-fold cross-validation using 80 patient CT volumes, we achieved 68.8 % Dice coefficient and 57.2 % Jaccard Index, comparable to or slightly better than published state-of-the-art methods.
The rights of this work are transferred to the extent transferable according to title 17 U.S.C. 105.
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
Okada, T., Linguraru, M.G., Yoshida, Y., Hor, M., Summers, R.M., Chen, Y., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: Abdominal Imaging. Computational and Clinical Applications (2012)
Shimizu, A., Kimoto, T., Kobatake, H., Nawano, S., Shinozaki, K.: Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int. J. Comput. Assist. Radiol. Surg. 5, 85–98 (2010)
Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32(7), 1723–1730 (2013)
Mahapatra, D., Schuffler, P., Tielbeek, J., Makanyanga, J., Stoker, J., Taylor, S., Vos, F., Buhmann, J.: Automatic detection and segmentation of Crohn’s disease tissues from abdominal MRI. IEEE Trans. Med. Imaging 32, 2332–2348 (2013)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pat. Ana. Mach. Intel. 34(11), 2274–2282 (2012)
Vincent, L., Soille, P.: Watersheds in digital spaces: an Efficient algorithm based on immersion simulations. IEEE Trans. Pat. Ana. Mach. Intel. 13, 83–598 (1991)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Liu, M., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: IEEE Conference on CVPR, pp. 2099–2104, (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
VLFEAT toolbox. http://www.vlfeat.org/overview/dsift.html
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Kim, J., Grauman, K.: Boundary preserving dense local regions. In: IEEE Conference on CVPR, pp. 1553–1560 (2011)
Gilinsky, A., Zelnik-Manor, I.: SIFTpack: a compact representation for efficient SIFT matching. In: IEEE Conference on ICCV, (2013)
Groeneveld, R., Meeden, G.: Measuring skewness and kurtosis. Statistician 33, 391–399 (1984)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Roth, H., Lu, L., Seff, A., Chery, K., Liu, J., Hoffman, J., Wang, S., Turkbey, E., Summers, R.M.: A New 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. http://arxiv-web3.library.cornell.edu/pdf/1406.2639v1.pdf (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Farag, A., Lu, L., Turkbey, E., Liu, J., Summers, R.M. (2014). A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham. https://doi.org/10.1007/978-3-319-13692-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-13692-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13691-2
Online ISBN: 978-3-319-13692-9
eBook Packages: Computer ScienceComputer Science (R0)