Semi-supervised image registration using deep learning

Alireza Sedghi; Jie Luo; Alireza Mehrtash; Steve Pieper; Clare M. Tempany; Tina Kapur; Parvin Mousavi; William M. Wells III

doi:10.1117/12.2513020

8 March 2019 Semi-supervised image registration using deep learning

Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III

Author Affiliations +

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109511G (2019) https://doi.org/10.1117/12.2513020
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Citation Download Citation

Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, and William M. Wells III "Semi-supervised image registration using deep learning", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511G (8 March 2019); https://doi.org/10.1117/12.2513020

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