Abstract
Detecting anatomical landmarks on structural magnetic resonance imaging (MRI) is an important medical computer-aid technique. However, for some brain anatomical landmarks detection, linear/non-linear registration with skull stripping across subjects is usually unavoidable. In this paper, we propose a novel method. Starting from the original MRI data, a series of 3D convolutional neural networks (cascaded 3D-CNNs) are adopted to iteratively update the predicted landmarks. Specially, the predicted landmarks of each 3D-CNN model are used to estimate the corresponding linear transformation matrix by linear square regression, which is very different from traditional registration methods. Based on the estimated matrix, we can use it to transform the original image for getting the new image for the next 3D-CNN model. With these cascaded 3D-CNNs and linear square regression, we can finally achieve registration and landmark detection.
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Acknowledgments
This work was supported in part by the National Key R&D Program of China under contract No. 2017YFB1002202, in part by the National Natural Science Foundation of China under Grants 61671422 and U1613211, in part by the MOE-Microsoft Key Laboratory of USTC. The authors would like to thank Dr. Dinggang Shen for the contributions on implementation.
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Wang, ZR., Yin, BC., Du, J., Liu, C., Tao, X., Hu, G. (2018). Fast and Robust Detection of Anatomical Landmarks Using Cascaded 3D Convolutional Networks Guided by Linear Square Regression. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_64
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