Abstract
Accurate and consistent segmentation of longitudinal brain magnetic resonance (MR) images is of great importance in studying brain morphological and functional changes over time. However, current available brain segmentation methods, especially deep learning methods, are mostly trained with cross-sectional brain images that might generate inconsistent results in longitudinal studies. To overcome this limitation, we present a novel coarse-to-fine spatio-temporal constrained deep learning model for consistent longitudinal segmentation based on limited labeled cross-sectional data with semi-supervised learning. Specifically, both segmentation smoothness and temporal consistency are imposed in the loss function. Moreover, brain structural changes over time are summarized as age constraint, to make the model better reflect the trends of longitudinal aging changes. We validate our proposed method on 53 sets of longitudinal T1-weighted brain MR images from ADNI, with an average of 4.5 time-points per subject. Both quantitative and qualitative comparisons with comparison methods demonstrate the superior performance of our proposed method.
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References
Chen, A., Yan, H.: An improved fuzzy c-means clustering for brain MR images segmentation. J. Med. Imaging Health Inf. 11(2), 386–390 (2021)
Coupé, P., Mansencal, B., Clément, M., Giraud, R., Manjon, J.V.: AssemblyNet: a large ensemble of CNNs for 3D whole brain MRI segmentation. NeuroImage 219, 117026 (2020)
Dadar, M., Collins, D.L.: BISON: Brain tissue segmentation pipeline using T1-weighted magnetic resonance images and a random forest classifier. Magn. Reson. Med. 85(4), 1881–1894 (2020)
Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 66–67 (2010)
Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Weiner, M.W.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2010)
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)
Novosad, P., Fonov, V., Collins, D.L., Initiative\(\dagger \), A.D.N.: Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks. Hum. Brain Mapp. 41(2), 309–327 (2020)
Sun, L., Shao, W., Wang, M., Zhang, D., Liu, M.: High-order feature learning for multi-atlas based label fusion: application to brain segmentation with MRI. IEEE Trans. Image Process. 29, 2702–2713 (2020)
Sun, L., Shao, W., Zhang, D., Liu, M.: Anatomical attention guided deep networks for ROI segmentation of brain MR images. IEEE Trans. Med. Imaging 39(6), 2000–2012 (2020)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 897–908 (1999)
Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–623 (2013)
Wu, J., Tang, X.: Brain segmentation based on multi-atlas and diffeomorphism guided 3D fully convolutional network ensembles. Pattern Recognit. 115, 107904 (2021)
Xue, Z., Shen, D., Davatzikos, C.: CLASSIC: consistent longitudinal alignment and segmentation for serial image computing. Neuroimage 30(2), 388–399 (2005)
Yu, Q., et al.: C2FNAS: coarse-to-fine neural architecture search for 3D medical image segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4125–4134 (2020)
Zhai, J., Li, H.: An improved full convolutional network combined with conditional random fields for brain MR image segmentation algorithm and its 3D visualization analysis. J. Med. Syst. 43(9), 1–10 (2019). https://doi.org/10.1007/s10916-019-1424-0
Zhang, W., et al.: Morphometric analysis of hippocampus and lateral ventricle reveals regional difference between cognitively stable and declining persons. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 14–18 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grants 61771397, and in part by the CAAI-Huawei MindSpore Open Fund under Grants CAAIXSJLJJ-2020-005B and China Postdoctoral Science Foundation under Grants BX2021333
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Wei, J., Shi, F., Cui, Z., Pan, Y., Xia, Y., Shen, D. (2021). Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_9
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DOI: https://doi.org/10.1007/978-3-030-87193-2_9
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