Abstract
Landmark detection in medical images is important for many clinical applications. Learning-based landmark detection is successful at solving some problems but it usually requires a large number of the annotated datasets for the training stage. In addition, traditional methods usually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetric images from a single example based on a one-shot learning method. It involves the iterative training of a shallow convolutional neural network combined with a 3D registration algorithm in order to perform automatic organ localization and landmark matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The results show that our one-shot learning scheme converges well and leads to a good accuracy of the landmark positions.
This work was partially funded by the French government, by the National Research Agency: ANR-15-IDEX-01, and by the grant AAP Sante 06 2017-260 DGADSH.
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References
Cheung, W., et al.: N-sift: n-dimensional scale invariant feature transform. IEEE Trans. Image Process. 18(9), 2012–2021 (2009)
Wörz, S., et al.: Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models. Media 10(1), 41–58 (2006)
Ferrari, R.J., Allaire, S., Hope, A., Kim, J., Jaffray, D., Pekar, V.: Detection of point landmarks in 3D medical images via phase congruency model. J. Braz. Comput. Soc. 17(2), 117–132 (2011). https://doi.org/10.1007/s13173-011-0032-8
Schmidt, S., et al.: Spine detection and labeling using a parts-based graphical model. In: IPMI, pp. 122–133 (2007)
Corso, J., et al.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: MICCAI, pp. 202–210 (2008)
Potesil, V., et al.: Personalization of pictorial structures for anatomical landmark localization. In: IPMI, pp. 333–345 (2011)
Shouhei, H., et al.: Automatic detection of over 100 anatomical landmarks in medical CT images. Media 35, 192–214 (2017)
Donner, R., et al.: Global localization of 3D anatomical structures by prefiltered hough forests and discrete optimization. Media 17, 1304–1314 (2013)
Mothes, O., et al.: One-shot learned priors in augmented active appearance models for anatomical landmark tracking. In: CVICG, pp. 85–104 (2019)
Suzani, A., et al.: Fast automatic vertebrae detection and localization in pathological CT scans. In: MICCAI, vol. 9351 (2015)
Liang, X., et al.: A deep learning framework for prostate localization in cone beam CT-guided radiotherapy. Med. Phys. 47(9), 4233–4240 (2020)
Ghesu, F., et al.: Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE TPAMI 41(1), 176–189 (2019)
Zhang, J., et al.: Detecting anatomical landmarks from limited medical imaging data using t2dl. IEEE TIP 26(10), 4753–4764 (2017)
Wu, D., et al.: One shot learning gesture recognition from RGBD images. In: 2012 IEEE CVPR Workshops, pp. 7–12 (2012)
Oriol, V., et al.: Matching networks for one shot learning. In: NIPS, pp. 3630–3638 (2016)
Jaklic, A., et al.: Moments of superellipsoids and their application to range image registration. IEEE Trans. Cybern. 33(4), 648–657 (2003)
Crisco, J.J., et al.: Efficient calculation of mass moments of inertia for segmented homogenous 3D objects. J. Biomech. 31(1), 97–101 (1997)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75759-7_39
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
Devira, Z., et al.: Variations in cochlear size of cochlear implant candidates. Int. Arch. Otorhinolaryngol. 23, 184–190 (2019)
Grewal, M., et al.: An end-to-end deep learning approach for landmark detection and matching in medical images. PBOI 11313, 1131–1328 (2020)
Gregory, K., et al.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop (2015)
Amirreza, S., et al.: One-shot learning for semantic segmentation (2017)
Chen, Z., et al.: Image deformation meta-networks for one-shot learning. In: IEEE CVPR, June 2019
Shruti, J., et al.: Improving siamese networks for one shot learning using kernel based activation functions. ArXiv, abs/1910.09798, 2019
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Wang, Z., Vandersteen, C., Raffaelli, C., Guevara, N., Patou, F., Delingette, H. (2021). One-Shot Learning for Landmarks Detection. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_15
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DOI: https://doi.org/10.1007/978-3-030-88210-5_15
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