Abstract
Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data heterogeneity. In particular, magnetic resonance images depend on a large number of acquisition protocol parameters as well as hardware and software characteristics that might differ between or even within institutions. In this work, we use a supervised image-to-image harmonization framework based on a conditional generative adversarial network to reduce inter-site differences in T1-weighted images using different dementia protocols. We investigate the use of different hybrid losses including standard voxel-wise distances and a more recent perceptual similarity metric, and how they relate to image similarity metrics and volumetric consistency in brain segmentation. In a test cohort of 30 multiprotocol patients affected by dementia, we show that despite improvements in terms of image similarity, the synthetic images generated do not necessarily result in reduced inter-site volumetric differences, therefore highlighting the mismatch between harmonization performance and the impact on the robustness of post-processing applications. Hence, our results suggest that traditional image similarity metrics such as PSNR or SSIM may poorly reflect the performance of different harmonization techniques in terms of improving cross-domain consistency.
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References
Schmitter, D., et al.: An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroImage Clin. 7, 7–17 (2015). https://doi.org/10.1016/j.nicl.2014.11.001
Fartaria, M.J., et al.: Automated detection of white matter and cortical lesions in early stages of multiple sclerosis. J. Magn. Reson. Imaging 43, 1445–1454 (2016). https://doi.org/10.1002/jmri.25095
Grøvik, E., Yi, D., Iv, M., Tong, E., Rubin, D., Zaharchuk, G.: Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J. Magn. Reson. Imaging 51, 175–182 (2020). https://doi.org/10.1002/jmri.26766
Haller, S., et al.: Basic MR sequence parameters systematically bias automated brain volume estimation. Neuroradiology 58(11), 1153–1160 (2016). https://doi.org/10.1007/s00234-016-1737-3
Hays, S.P., Zuo, L., Carass, A., Prince, J. Evaluating the impact of MR image contrast on whole brain segmentation. In: Medical Imaging 2022: Image Processing, vol. 12032, pp. 122–126. SPIE, April 2022
Dewey, B.E., et al.: DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging 64, 160–170 (2019). https://doi.org/10.1016/j.mri.2019.05.041
Dewey, B.E., et al.: A disentangled latent space for cross-site MRI harmonization. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 720–729. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_70
Guan, H., Liu, Y., Yang, E., Yap, P.T., Shen, D., Liu, M.: Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med. Image Anal. 71, 102076 (2021). https://doi.org/10.1016/j.media.2021.102076
Mirzaalian, H., et al.: Harmonizing diffusion MRI data across multiple sites and scanners. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 12–19. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_2
Knyaz, V.A., Kniaz, V.V., Remondino, F.: Image-to-voxel model translation with conditional adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 601–618. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_37
Yang, Q., Li, N., Zhao, Z., Fan, X., Chang, E.I.C., Xu, Y.: MRI cross-modality image-to-image translation. Sci. Rep. 10, 1–18 (2020). https://doi.org/10.1038/s41598-020-60520-6
Dar, S.U.H., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Cukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38, 2375–2388 (2019). https://doi.org/10.1109/TMI.2019.2901750
Kaiser, B., Albarqouni, S.: MRI to CT Translation with GANs. (2019)
Jung, M.M., Berg, B. Van Den, Postma, E., Huijbers, W.: Inferring PET from MRI with pix2pix. In: Benelux Conference on Artificial Intelligence, pp. 1–9 (2018)
Armanious, K., et al.: MedGAN: medical image translation using GANs. Comput. Med. Imaging Graph. 79 (2020). https://doi.org/10.1016/j.compmedimag.2019.101684
Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L.: Random forest regression for magnetic resonance image synthesis. Med. Image Anal. 35, 475–488 (2017). https://doi.org/10.1016/j.media.2016.08.009
Moyer, D., Ver Steeg, G., Tax, C.M.W., Thompson, P.M.: Scanner invariant representations for diffusion MRI harmonization. Magn. Reson. Med. 84, 2174–2189 (2020). https://doi.org/10.1002/mrm.28243
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. Cvpr2018 13 (2018)
Wyman, B.T., et al.: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s Dement. 9, 332–337 (2013). https://doi.org/10.1016/j.jalz.2012.06.004
Tustison, N.J., Avants, B.B., Cook, P.A., Gee, J.C.: N4ITK : improved N3 bias correction with robust b-spline approximation. In: Proceedings of ISBI 2010, pp. 708–711 (2010)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging. 29, 196–205 (2010). https://doi.org/10.1109/TMI.2009.2035616
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B., Taylor, J.E.: Interpretable whole-brain prediction analysis with GraphNet. Neuroimage 72, 304–321 (2013). https://doi.org/10.1016/j.neuroimage.2012.12.062
Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35, 73–101 (1964). https://doi.org/10.1214/aoms/1177703732
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2623–2631 (2019). https://doi.org/10.1145/3292500.3330701
Mccormick, M., Liu, X., Jomier, J., Marion, C., Ibanez, L.: ITK: enabling reproducible research and open science. Front. Neuroinform. 8, 1–11 (2014). https://doi.org/10.3389/fninf.2014.00013
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
Vaserstein, L.N.: Markov processes over denumerable products of spaces, describing large systems of automata, Probl. Peredachi. Inf. 5(3), 64–72 (1969)
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This work was co-financed by Innosuisse (Grant 43087.1 IP-LS).
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Ravano, V. et al. (2022). Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_9
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