Abstract
Manual segmentations of anatomical regions in the brain are time consuming and costly to acquire. In a clinical trial setting, this is prohibitive and automated methods are needed for routine application. We propose a deep-learning architecture that automatically delineates sub-cortical regions in the brain (example biomarkers for monitoring the development of Huntington’s disease). Neural networks, despite typically reaching state-of-the-art performance, are sensitive to differing scanner protocols and pre-processing methods. To address this challenge, one can pre-train a model on an existing data set and then fine-tune this model using a small amount of labelled data from the target domain. This work investigates the impact of the pre-training task and the amount of data required via a systematic study. We show that use of just a few samples from the same task (but a different domain) can achieve state-of-the-art performance. Further, this pre-training task utilises automated labels, meaning the pipeline requires very few manually segmented data points. On the other hand, using a different task for pre-training is shown to be less successful. We then conclude, by showing that, whilst fine-tuning is very powerful for a specific data distribution, models developed in this fashion are considerably more fragile when used on completely unseen data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). software available from tensorflow.org https://www.tensorflow.org/
Alex, V., Vaidhya, K., Thirunavukkarasu, S., Kesavadas, C., Krishnamurthi, G.: Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation. J. Med. Imaging 4(4), 041311 (2017)
Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33(3), 261–274 (2010). https://doi.org/10.1007/s10462-010-9155-0
Bowles, C., et al.: GAN augmentation: augmenting training data using generative adversarial networks. arXiv preprint arXiv:1810.10863 (2018)
Brusini, I., Lindberg, O., Muehlboeck, J.S., Smedby, Ö., Westman, E., Wang, C.: Shape information improves the cross-cohort performance of deep learning-based segmentation of the hippocampus. Front. Neurosci. 14, 15 (2020)
Cheplygina, V., de Bruijne, M., Pluim, J.P.: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280–296 (2019)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Freeborough, P.A., Fox, N.C.: The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans. Med. Imaging 16(5), 623–629 (1997)
Georgiou-Karistianis, N., Hannan, A.J., Egan, G.F.: Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington’s disease. Brain Res. Rev. 58(1), 209–225 (2008)
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
Giorgio, A., De Stefano, N.: Clinical use of brain volumetry. J. Magn. Reson. Imaging 37(1), 1–14 (2013)
Henley, S.M., Bates, G.P., Tabrizi, S.J.: Biomarkers for neurodegenerative diseases. Curr. Opin. Neurol. 18(6), 698–705 (2005)
Jack Jr., C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 27(4), 685–691 (2008)
Johnson, E.B., et al.: Recommendations for the use of automated gray matter segmentation tools: evidence from Huntington’s disease. Front. Neurol. 8, 519 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krivov, E., Pisov, M., Belyaev, M.: MRI augmentation via elastic registration for brain lesions segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 369–380. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_32
Ledig, C., Schuh, A., Guerrero, R., Heckemann, R.A., Rueckert, D.: Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Sci. Rep. 8(1), 1–16 (2018)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)
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
Roy, A.G., Conjeti, S., Navab, N., Wachinger, C., Initiative, A.D.N., et al.: QuickNAT: a fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage 186, 713–727 (2019)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Weese, J., Lorenz, C.: Four challenges in medical image analysis from an industrial perspective. Med. Image Anal. 33, 44–49 (2016)
Wolz, R., Aljabar, P., Hajnal, J.V., Hammers, A., Rueckert, D., Initiative, A.D.N., et al.: LEAP: learning embeddings for atlas propagation. NeuroImage 49(2), 1316–1325 (2010)
Zavala-Romero, O., et al.: Segmentation of prostate and prostate zones using deep learning. Strahlentherapie und Onkologie (2020). https://doi.org/10.1007/s00066-020-01607-x
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Acknowledgements
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Weatheritt, J., Rueckert, D., Wolz, R. (2020). Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitations. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-52791-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52790-7
Online ISBN: 978-3-030-52791-4
eBook Packages: Computer ScienceComputer Science (R0)