Title
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation.
Abstract
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
Year
DOI
Venue
2017
10.1007/978-3-319-66179-7_59
MICCAI
DocType
Volume
ISSN
Conference
abs/1702.07841
Medical Image Computing and Computer-Assisted Intervention 2017, Vol 10435, 516-524
Citations 
PageRank 
References 
24
0.75
12
Authors
14
Name
Order
Citations
PageRank
Mohsen Ghafoorian168127.23
Alireza Mehrtash2445.69
Tina Kapur339045.30
Nico Karssemeijer4992122.49
Elena Marchiori5321.96
Mehran Pesteie6445.01
Charles R. G. Guttmann729643.20
Frank-Erik de Leeuw8543.84
Clare M Tempany962945.11
Bram van Ginneken104979307.23
Andriy Fedorov1117116.54
Purang Abolmaesumi124911.94
Bram Platel1324521.42
William M. Wells III145267833.10