FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation
Tóm tắt
Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.
Tài liệu tham khảo
Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 46(3), 726–738.
Artaechevarria, X., Muñoz-Barrutia, A., and Ortiz-de-Solorzano, C.(2008). “Efficient classifier generation and weighted voting for atlas-based segmentation: Two small steps faster and closer to the combination oracle,” SPIE Medical Imaging, 69141W–69141W-9.
Artaechevarria, X., Munoz-Barrutia, A., & Ortiz-de-Solorzano, C. (2009). Combination strategies in multi-atlas image segmentation: Application to brain MR data. Medical Imaging, IEEE Transactions on, 28(8), 1266–1277.
Asman, A. J., & Landman, B. A. (2012). Formulating spatially varying performance in the statistical fusion framework. Medical Imaging, IEEE Transactions on, 31(6), 1326–1336.
Asman, A. J., & Landman, B. A. (2013). Non-local statistical label fusion for multi-atlas segmentation. Medical Image Analysis, 17(2), 194–208.
Asman, A. J., & Landman, B. A. (2014). Hierarchical performance estimation in the statistical label fusion framework. Medical Image Analysis, 18(7), 1070–1081.
Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.
Bai, W., Shi, W., O'Regan, D. P., et al. (2013). A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac MR images. Medical Imaging, IEEE Transactions on, 32(7), 1302–1315.
Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2015). Multi-atlas segmentation with augmented features for cardiac MR images. Medical Image Analysis, 19(1), 98–109.
Benkarim, O. M., Piella, G., Ballester, M. A. G., et al. (2017). Discriminative confidence estimation for probabilistic multi-atlas label fusion. Medical Image Analysis, 42, 274–287.
Boccardi, M., Bocchetta, M., Morency, F. C., Collins, D. L., Nishikawa, M., Ganzola, R., Grothe, M. J., Wolf, D., Redolfi, A., Pievani, M., Antelmi, L., Fellgiebel, A., Matsuda, H., Teipel, S., Duchesne, S., Jack CR Jr, Frisoni, G. B., & EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Segmentation and for the Alzheimer's Disease Neuroimaging Initiative. (2015). Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimers Dement, 11(2), 175–183.
Cao, Y., Yuan, Y., Li, X. et al. (2011). “Segmenting images by combining selected atlases on manifold,” International Conference on Medical Image Computing and Computer-Assisted Intervention, 272–279.
Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. A. (2018). VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170, 446–455.
Commowick, O., Akhondi-Asl, A., & Warfield, S. K. (2012). Estimating a reference standard segmentation with spatially varying performance parameters: Local MAP STAPLE. Medical Imaging, IEEE Transactions on, 31(8), 1593–1606.
Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2), 940–954.
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., Davatzikos, C., & Alzheimer's Neuroimaging Initiative. (2016). MUSE: MUlti-atlas region segmentation utilizing ensembles of registration algorithms and parameters, and locally optimal atlas selection. NeuroImage, 127, 186–195.
Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., & Heng, P. A. (2017). 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41, 40–54.
A. K. H. Duc, M. Modat, K. K. Leung et al., “Manifold learning for atlas selection in multi-atlas-based segmentation of hippocampus,” Medical Imaging 2012: Image Processing, 8314, 83140Z (2012).
Fang, L., Zhang, L., Nie, D. et al. (2017). “Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks,” International Workshop on Patch-based Techniques in Medical Imaging, 12–19.
Gu, J., Wang, Z., Kuen, J., et al. (2017). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
Haber, E., & Modersitzki, J. (2004). Numerical methods for volume preserving image registration. Inverse Problems, 20(5), 1621.
Hao, Y., Wang, T., Zhang, X., Duan, Y., Yu, C., Jiang, T., Fan, Y., & Alzheimer's Disease Neuroimaging Initiative. (2014). Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation. Human Brain Mapping, 35(6), 2674–2697.
Haom, Y., Liu, J., Duan, Y. et al. (2012). “Local label learning (L3) for multi-atlas based segmentation,” SPIE Medical Imaging, 83142E-83142E-8.
He, K., Zhang, X., Ren, S. et al. (2016a). “Identity mappings in deep residual networks,” European Conference on Computer Vision, 630–645.
He, K., Zhang, X., Ren, S. et al. (2016b). “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006). Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33(1), 115–126.
Huang, G., Liu, Z., Van Der Maaten, L. et al. (2017). “Densely connected convolutional networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
Iglesias, J. E., & Sabuncu, M. R. (2015). Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1), 205–219.
Jack, C. R., Bernstein, M. A., Fox, N. C., et al. (2008). The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.
Jafari-Khouzani, K., Elisevich, K. V., Patel, S., & Soltanian-Zadeh, H. (2011). Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques. Neuroinformatics, 9(4), 335–346.
Jia, Y., Shelhamer, E., Donahue, J. et al. (2014). “Caffe: Convolutional architecture for fast feature embedding,” Proceedings of the 22nd ACM international conference on Multimedia, 675–678.
Jorge Cardoso, M., Leung, K., Modat, M., Keihaninejad, S., Cash, D., Barnes, J., Fox, N. C., Ourselin, S., & Alzheimer’s Disease Neuroimaging Initiative. (2013). STEPS: Similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Medical Image Analysis, 17(6), 671–684.
Langerak, T. R., Berendsen, F. F., Van der Heide, U. A., et al. (2013). Multiatlas-based segmentation with preregistration atlas selection. Medical Physics, 40(9), 091701.
Liao, S., Gao, Y., & Shen, D. (2012). Sparse patch based prostate segmentation in CT images. Medical Image Computing and Computer-Assisted Intervention–MICCAI, 2012, 385–392.
Liao, S., Gao, Y., Lian, J., et al. (2013). Sparse patch-based label propagation for accurate prostate localization in CT images. Medical Imaging, IEEE Transactions on, 32(2), 419–434.
Long, J., Shelhamer, E., and Darrell, T. (2015). “Fully convolutional networks for semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431–3440.
Lötjönen, J. M. P., Wolz, R., Koikkalainen, J. R., et al. (2010). Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage, 49(3), 2352–2365.
Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” 3D Vision (3DV), 2016 Fourth International Conference on, 565–571.
Rohlfing, T., Brandt, R., Menzel, R., & Maurer CR Jr. (2004). Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage, 21(4), 1428–1442.
Ronneberger, O., Fischer, P., and Brox, T. (2015). “U-net: Convolutional networks for biomedical image segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241.
Rousseau, F., Habas, P. A., & Studholme, C. (2011). A supervised patch-based approach for human brain labeling. Medical Imaging, IEEE Transactions on, 30(10), 1852–1862.
Sabuncu, M. R., Yeo, B. T. T., Van Leemput, K., et al. (2010). A generative model for image segmentation based on label fusion. Medical Imaging, IEEE Transactions on, 29(10), 1714–1729.
Sanroma, G., Wu, G., Gao, Y., et al. (2014). Learning to rank atlases for multiple-atlas segmentation. Medical Imaging, IEEE Transactions on, 33(10), 1939–1953.
Shamsolmoali, P., Zhang, J., & Yang, J. (2019). Image super resolution by dilated dense progressive network. Image and Vision Computing, 88, 9–18.
Wang, H., Suh, J. W., Das, S. et al. (2011). “Regression-based label fusion for multi-atlas segmentation,” Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 1113–1120.
Wang, H., Suh, J. W., Das, S. R., et al. (2013). Multi-atlas segmentation with joint label fusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(3), 611–623.
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on, 23(7), 903–921.
Xu, B., Ye, H., Zheng, Y., Wang, H., Luwang, T., & Jiang, Y. G. (2019). Dense dilated network for video action recognition. IEEE Transactions on Image Processing, 28(10), 4941–4953.
Yang, H., Sun, J., Li, H. et al. (2017). “Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images,” arXiv preprint arXiv:1709.09641.
Yu, F., and Koltun, V. (2015). “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122.
Yu, L., Yang, X., Chen, H. et al. (2017). “Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images,” AAAI, 66–72.
Zaffino, P., Ciardo, D., Raudaschl, P., et al. (2018). Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases? Physics in Medicine & Biology, 63(12), 12NT01.
Zhu, H., Cheng, H., and Fan, Y. (2015). “Random local binary pattern based label learning for multi-atlas segmentation,” SPIE Medical Imaging, 94131B-94131B-8.
Zhu, H., Cheng, H., Yang, X., Fan, Y., & Alzheimer’s Disease Neuroimaging Initiative. (2017). Metric learning for multi-atlas based segmentation of Hippocampus. Neuroinformatics, 15(1), 41–50.