PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

Mauricio Orbes-Arteaga; Lauge Sørensen; Jorge  Cardoso; Marc  Modat; Sebastien Ourselin; Stefan Horst Sommer; Mads Nielsen; Christian Igel; Akshay Sadananda Uppinakudru Pai

doi:10.1117/12.2512520

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

Mauricio Orbes-Arteaga, Lauge Sørensen, Jorge Cardoso , Marc Modat , Sebastien Ourselin, Stefan Horst Sommer, Mads Nielsen, Christian Igel, Akshay Sadananda Uppinakudru Pai

1 Citation (Scopus)

Abstract

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) – a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. The main advantage of PADDIT is the ability to produce transformations that capture the morphological variability in the training data. To this end, PADDIT constructs a mean template which represents the main shape tendency of the training data. A Hamiltonian Monte Carlo(HMC) scheme is used to sample transformations which warp the training images to the generated mean template. Augmented images are created by warping the training images using the sampled transformations. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation (0.2 and 0.15 higher dice accuracy respectively) in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.

Original language	English
Title of host publication	Proceedings, SPIE 10949, Medical Imaging 2019: Image Processing
Number of pages	6
Publisher	SPIE - International Society for Optical Engineering
Publication date	2019
Chapter	109490S
DOIs	https://doi.org/10.1117/12.2512520
Publication status	Published - 2019
Event	SPIE Medical Imaging - San Diego, United States Duration: 16 Feb 2019 → 21 Feb 2019

Conference

Conference	SPIE Medical Imaging
Country/Territory	United States
City	San Diego
Period	16/02/2019 → 21/02/2019

Access to Document

10.1117/12.2512520

Cite this

Orbes-Arteaga, M., Sørensen, L., Cardoso , J., Modat , M., Ourselin, S., Sommer, S. H., Nielsen, M., Igel, C., & Pai, A. S. U. (2019). PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation. In Proceedings, SPIE 10949, Medical Imaging 2019: Image Processing SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2512520

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation. / Orbes-Arteaga, Mauricio; Sørensen, Lauge; Cardoso , Jorge et al.
Proceedings, SPIE 10949, Medical Imaging 2019: Image Processing . SPIE - International Society for Optical Engineering, 2019.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Orbes-Arteaga, M, Sørensen, L, Cardoso , J, Modat , M, Ourselin, S, Sommer, SH , Nielsen, M , Igel, C & Pai, ASU 2019, PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation. in Proceedings, SPIE 10949, Medical Imaging 2019: Image Processing . SPIE - International Society for Optical Engineering, SPIE Medical Imaging, San Diego, United States, 16/02/2019. https://doi.org/10.1117/12.2512520

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abstract = "For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) – a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. The main advantage of PADDIT is the ability to produce transformations that capture the morphological variability in the training data. To this end, PADDIT constructs a mean template which represents the main shape tendency of the training data. A Hamiltonian Monte Carlo(HMC) scheme is used to sample transformations which warp the training images to the generated mean template. Augmented images are created by warping the training images using the sampled transformations. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation (0.2 and 0.15 higher dice accuracy respectively) in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans. ",

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