Abstract
Computing pancreatic morphology in 3D radiological scans could provide significant insight about a medical condition. However, segmenting the pancreas in magnetic resonance imaging (MRI) remains challenging due to high inter-patient variability. Also, the resolution and speed of MRI scanning present artefacts that blur the pancreas boundaries between overlapping anatomical structures. This paper proposes a dual-stage automatic segmentation method: (1) a deep neural network is trained to address the problem of vague organ boundaries in high class-imbalanced data. This network integrates a novel loss function to rigorously optimise boundary delineation using the modified Hausdorff metric and a sinusoidal component; (2) Given a test MRI volume, the output of the trained network predicts a sequence of targeted 2D pancreas classes that are reconstructed as a volumetric binary mask. An energy-minimisation approach fuses a learned digital contrast model to suppress the intensities of non-pancreas classes, which, combined with the binary volume performs a refined segmentation in 3D while revealing dense boundary detail. Experiments are performed on two diverse MRI datasets containing 180 and 120 scans, in which the proposed approach achieves a mean Dice score of 84.1 ± 4.6% and 85.7 ± 2.3%, respectively. This approach is statistically stable and outperforms state-of-the-art methods on MRI.
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References
Macauley, M., Percival, K., Thelwall, P.E., Hollingsworth, K.G., Taylor, R.: Altered volume, morphology and composition of the pancreas in type 2 diabetes. PLoS ONE 10(5), 1–14 (2015)
Omeri, A., et al.: Contour variations of the body and tail of the pancreas: evaluation with MDCT. J. Radiol. 35(6), 310–318 (2017)
Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 442–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_51
Okada, T., et al.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) ABD-MICCAI 2011. LNCS, vol. 7029, pp. 173–180. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28557-8_22
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(1), 85–98 (2010)
Okada, T., Linguraru, M.G., Hori, M., Summers, R., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med. Image Anal. 26, 1–18 (2015)
Cai, J., Lu, L., Xie, Y., Xing, F., Yang, L.: Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. CoRR, abs/1707.04912 (2017)
Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E., Summers, R.M.: A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Trans. Image 26(1), 386–399 (2017)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv:1511.00561 (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
Gibson, E., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-Networks. IEEE Trans. Med. Imaging 37, 1822–1834 (2018)
Asaturyan, H., Gligorievski, A., Villarini, B.: Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation. Comput. Med. Imaging Graph. 75, 1–13 (2019)
Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE ICCV, pp. 1395–1403 (2015)
Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650–2658 (2015)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621 (2017)
Asaturyan, H., Villarini, B.: Hierarchical framework for automatic pancreas segmentation in MRI using continuous max-flow and min-cuts approach. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 562–570. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_64
Yuan, J., Bae, E., Tai, X.-C.: A study on continuous max-flow and min-cut approaches. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2217–2224 (2010)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth Fourth International Conference on 3D Vision, pp. 565–571 (2016)
Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 234–244. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_22
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Asaturyan, H., Thomas, E.L., Fitzpatrick, J., Bell, J.D., Villarini, B. (2019). Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function. 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_4
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DOI: https://doi.org/10.1007/978-3-030-32692-0_4
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