Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation.
PLoS One
; 18(2): e0282110, 2023.
Article
em En
| MEDLINE
| ID: mdl-36827289
ABSTRACT
PURPOSE:
This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article