Abstract
Fully convolutional network has predicted multiple class dense outputs in CT image labels and obtained significant improvements in segmentation tasks. In this paper, we present a joint multi-path fully convolutional network (MFCN) with random forests (RF) architecture for abdominal organs segmentation automatically. First, in coarse segmentation step, three FCNs are trained respectively with three orthogonal directions which consider contextual and spatial information of fusion layers adequately. In classification step, using features extracted from different layers of network and normalizing them to mean value as supervoxel representation to train RF. This allows the computation of supervoxel at each orientation achieve high efficiency. Finally, we aggregate the results of MFCN and RF on voxel-wise and perform conditional random fields (CRF) focuses on smoothing borders of fine segmentation regions. We exceeds the state-of-the-art methods and get achievable DSC values for our work is 90.1%, 88.4%, 88.0%, 88.6% represent liver, right and left kidney, spleen respectively.
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References
Ling, H., Zhou, S.K., Zheng, Y., et al.: Hierarchical, learning-based automatic liver segmentation. In: Computer Vision and Pattern Recognition 2008. CVPR, pp. 1–8. IEEE (2008)
Kitrungrotsakul, T., Han, X.H., Iwamoto, Y., et al.: Robust hepatic vessel segmentation using multi deep convolution networks, In: Proceedings of SPIE, vol. 10137, Medical Imaging 2017, Florida, USA (2017)
Foruzan, A.H., Chen, Y.W., Zoroofi, R.A., et al.: Segmentation of liver in low-contrast images using k-means clustering and geodesic active contour algorithms. IEICE Trans. Inf. Syst. 96(4), 798–807 (2013)
Xu, Z., Li, B., Panda, S., et al.: Shape-constrained multi-atlas segmentation of spleen in CT. In: Proceedings of SPIE–the International Society for Optical Engineering, vol. 9034, p. 903446. NIH Public Access (2014)
Okada, T., Linguraru, M.G., Yoshida, Y., et al.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: International MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging, pp. 173–180. Springer, Heidelberg (2011)
Glocker, B., Pauly, O., Konukoglu, E., et al.: Joint classification-regression forests for spatially structured multi-object segmentation. In: Computer Vision–ECCV 2012, pp. 870–881 (2012)
Liu, X., Linguraru, M.G., Yao, J., et al.: Organ pose distribution model and an MAP framework for automated abdominal multi-organ localization. In: International Workshop on Medical Imaging and Virtual Reality, pp. 393–402. Springer, Heidelberg (2010)
Shimizu, A., Ohno, R., Ikegami, T., et al.: Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int. J. Comput. Assist. Radiol. Surg. 2(3–4), 135–142 (2007)
Roth, H.R., Lu, L., Farag, A., et al.: Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 556–564. Springer, Heidelberg (2015)
Zhou, X., Ito, T., Takayama, R., et al.: First trial and evaluation of anatomical structure segmentations in 3D CT images based only on deep learning. Med. Imaging Inf. Sci. 33(3), 69–74 (2016)
Dou, Q., Chen, H., Jin, Y., et al.: 3D deeply supervised network for automatic liver segmentation from CT volumes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 149–157. Springer, Heidelberg (2016)
Sharif Razavian, A., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Socher, R., Huval, B., Bath, B., et al.: Convolutional-recursive deep learning for 3D object classification. In: Advances in Neural Information Processing Systems, pp. 656–664 (2012)
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)
Ganin, Y., Lempitsky, V.: N^ 4-fields: neural network nearest neighbor fields for image transforms. In: Asian Conference on Computer Vision, pp. 536–551. Springer, Cham (2014)
Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. In: International Conference on Machine Learning, pp. 82–90 (2014)
Ben-Cohen, A., Diamant, I., Klang, E., et al.: Fully convolutional network for liver segmentation and lesions detection. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 77–85. Springer, Heidelberg (2016)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Eighteenth International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Advances in Neural Information Processing Systems, pp. 109–117 (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Yang, Y., Jiang, H., Sun, Q.: A multiorgan segmentation model for CT volumes via full convolution-deconvolution network. Biomed. Res. Int. 2017, 6941306 (2017)
Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Prasoon, A., Petersen, K., Igel, C., et al.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 246–253. Springer, Heidelberg (2013)
Pereira, S., Pinto, A., Alves, V., et al.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 131–143. Springer, Cham (2015)
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This work was supported by the National Natural Science Foundation of China (No. 61472073, No. 61272176).
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Yang, Y., Jiang, H., Chen, Y. (2019). Abdominal Organs Segmentation Based on Multi-path Fully Convolutional Network and Random Forests. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Interactive Multimedia Systems and Services. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-319-92231-7_21
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DOI: https://doi.org/10.1007/978-3-319-92231-7_21
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