Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitations

Weatheritt J, Rueckert D, Wolz R (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Communications in Computer and Information Science Springer Verlag

Publisher: Springer

Book Volume: 1248 CCIS

Pages Range: 118-130

Conference Proceedings Title: Communications in Computer and Information Science

Event location: Oxford, GBR

ISBN: 9783030527907

DOI: 10.1007/978-3-030-52791-4_10

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.

Involved external institutions

Ixico Technologies Limited GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Weatheritt, J., Rueckert, D., & Wolz, R. (2020). Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitations. In Bartlomiej W. Papiez, Ana I.L. Namburete, Mohammad Yaqub, J. Alison Noble, Mohammad Yaqub (Eds.), Communications in Computer and Information Science (pp. 118-130). Oxford, GBR: Springer.

MLA:

Weatheritt, Jack, Daniel Rueckert, and Robin Wolz. "Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitations." Proceedings of the 24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020, Oxford, GBR Ed. Bartlomiej W. Papiez, Ana I.L. Namburete, Mohammad Yaqub, J. Alison Noble, Mohammad Yaqub, Springer, 2020. 118-130.

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