Train smarter, not harder: learning deep abdominal CT registration on scarce data

• URL: http://arxiv.org/abs/2211.15717v2
• Date: Wed, 30 Nov 2022 09:55:03 GMT
• Title: Train smarter, not harder: learning deep abdominal CT registration on scarce data
• Authors: Javier P\'erez de Frutos, Andr\'e Pedersen, Egidijus Pelanis, David Bouget, Shanmugapriya Survarachakan, Thomas Lang{\o}, Ole-Jakob Elle, Frank Lindseth
• Abstract summary: We explore training strategies to improve convolutional neural network-based image-to-image registration for abdominal imaging. Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance.
• Score: 0.8179387741893692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

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