Abstract
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop in the model’s performance. In medical imaging, there can be many sources of domain shift such as different scanners or scan protocols, different pathologies in the patient population, anatomical differences in the patient population (e.g. men vs women) etc. Therefore, in order to train models that have good generalization performance, it is important to be aware of the domain shift problem, its potential causes and to devise ways to address it. In this paper, we study the effect of domain shift on left and right ventricle blood pool segmentation in short axis cardiac MR images. Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups. The training is performed with nnUNet. The results show that scanner differences cause a greater drop in performance compared to changing the pathology group, and that the impact of domain shift is greater on right ventricle segmentation compared to left ventricle segmentation. Increasing the number of training subjects increased cross-scanner performance more than in-scanner performance at small training set sizes, but this difference in improvement decreased with larger training set sizes. Training models using data from multiple scanners improved cross-domain performance.
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References
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?, IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the MandMs challenge. IEEE Trans. Med. Imaging, pp. 1–1 (2021)
Finlayson, S.G., et al.: The clinician and dataset shift in artificial intelligence. New England J. Med. 385(3), 283–286 (2021)
Full, P.M., Isensee, F., Jäger, P.F., Maier-Hein, K.: Studying robustness of semantic segmentation under domain shift in cardiac MRI. In: Puyol Anton, E., et al. (eds.) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, pp. 238–249. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_24
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
von Knobelsdorff-Brenkenhoff, F., Pilz, G., Schulz-Menger, J.: Representation of cardiovascular magnetic resonance in the AHA/ACC guidelines. J. Cardiovascular Magn. Reson. 19(1), 70 (2017)
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset shift in machine learning. The MIT Press (2009)
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.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ruijsink, B.,et al.: Quality-aware semi-supervised learning for CMR segmentation. In: Puyol Anton, E., et al. (eds.) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, pp. 97–107. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_10
Ruijsink, B., et al.: Fully automated, quality-controlled cardiac analysis from CMR: Validation and large-scale application to characterize cardiac function. JACC: Cardiov. Imaging 13(3), 684–695 (2020)
Scannell, C.M., Chiribiri, A., Veta, M.: Domain-adversarial learning for multi-centre, multi-vendor, and multi-disease cardiac MR image segmentation. In: Puyol Anton, E., et al. (eds.) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, pp. 228–237. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_23
Wang, J., Huang, H., Chen, C., Ma, W., Huang, Y., Ding, X.: Multi-sequence cardiac MR segmentation with adversarial domain adaptation network. In: Pop, M., et al. (eds.) Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, pp. 254–262. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_27
Acknowledgements
This work was funded by the Engineering and Physical Sciences Research Council (EPSRC) programme grant ‘SmartHeart’ (EP/P001009/1) and supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. The research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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Ugurlu, D. et al. (2022). The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_7
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DOI: https://doi.org/10.1007/978-3-030-93722-5_7
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