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Semi-supervised image registration using deep learning
Author(s): Alireza Sedghi; Jie Luo; Alireza Mehrtash; Steve Pieper; Clare M. Tempany; Tina Kapur; Parvin Mousavi; William M. Wells
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Paper Abstract

Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning such metrics from roughly aligned training data. Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expense of increased variance), while random perturbations to the data, i.e. dithering, ensures that the metric has a single mode, and is amenable to registration by optimization. Evaluation is performed on the task of registration on separate unseen test image pairs. The results demonstrate the feasibility of learning a useful deep metric from substantially misaligned training data, in some cases, the results are significantly better than from Mutual Information. Data augmentation via dithering is, therefore, an effective strategy for discharging the need for well-aligned training data; this brings deep learning based registration from the realm of supervised to semi-supervised machine learning.

Paper Details

Date Published: 8 March 2019
PDF: 6 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511G (8 March 2019); doi: 10.1117/12.2513020
Show Author Affiliations
Alireza Sedghi, Queen's Univ. (Canada)
Jie Luo, Brigham and Women's Hospital (United States)
The Univ. of Tokyo (Japan)
Alireza Mehrtash, Brigham and Women's Hospital (United States)
Univ. of British Columbia (Canada)
Steve Pieper, Brigham and Women's Hospital (United States)
Clare M. Tempany, Brigham and Women's Hospital (United States)
Tina Kapur, Brigham and Women's Hospital (United States)
Parvin Mousavi, Queen's Univ. (Canada)
William M. Wells, Brigham and Women's Hospital (United States)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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