Deep learning based registration using spatial gradients and noisy segmentation labels

• URL: http://arxiv.org/abs/2010.10897v2
• Date: Fri, 9 Apr 2021 08:42:32 GMT
• Title: Deep learning based registration using spatial gradients and noisy segmentation labels
• Authors: Th\'eo Estienne, Maria Vakalopoulou, Enzo Battistella, Alexandre Carr\'e, Th\'eophraste Henry, Marvin Lerousseau, Charlotte Robert, Nikos Paragios and Eric Deutsch
• Abstract summary: deep learning based approaches became quite popular, providing fast and performing registration strategies. Our work relies on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar. Our method reports a mean dice of $0.64$ for task 3 and $0.85$ for task 4 on the test sets, taking third place on the challenge.
• Score: 52.78503776563559
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of $0.64$ for task 3 and $0.85$ for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and \https://github.com/TheoEst/hippocampus_registration.

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