TY - GEN
T1 - Image Reconstruction in a Manifold of Image Patches
T2 - 2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
AU - Gomez, Alberto
AU - Zimmer, Veronika
AU - Toussaint, Nicolas
AU - Wright, Robert
AU - Clough, James R.
AU - Khanal, Bishesh
AU - van Poppel, Milou P.M.
AU - Skelton, Emily
AU - Matthews, Jackie
AU - Schnabel, Julia A.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - We propose an image reconstruction framework to combine a large number of overlapping image patches into a fused reconstruction of the object of interest, that is robust to inconsistencies between patches (e.g. motion artefacts) without explicitly modelling them. This is achieved through two mechanisms: first, manifold embedding, where patches are distributed on a manifold with similar patches (where similarity is defined only in the region where they overlap) closer to each other. As a result, inconsistent patches are set far apart in the manifold. Second, fusion, where a sample in the manifold is mapped back to image space, combining features from all patches in the region of the sample. For the manifold embedding mechanism, a new method based on a Convolutional Variational Autoencoder (β -VAE) is proposed, and compared to classical manifold embedding techniques: linear (Multi Dimensional Scaling) and non-linear (Laplacian Eigenmaps). Experiments using synthetic data and on real fetal ultrasound images yield fused images of the whole fetus where, in average, β -VAE outperforms all the other methods in terms of preservation of patch information and overall image quality.
AB - We propose an image reconstruction framework to combine a large number of overlapping image patches into a fused reconstruction of the object of interest, that is robust to inconsistencies between patches (e.g. motion artefacts) without explicitly modelling them. This is achieved through two mechanisms: first, manifold embedding, where patches are distributed on a manifold with similar patches (where similarity is defined only in the region where they overlap) closer to each other. As a result, inconsistent patches are set far apart in the manifold. Second, fusion, where a sample in the manifold is mapped back to image space, combining features from all patches in the region of the sample. For the manifold embedding mechanism, a new method based on a Convolutional Variational Autoencoder (β -VAE) is proposed, and compared to classical manifold embedding techniques: linear (Multi Dimensional Scaling) and non-linear (Laplacian Eigenmaps). Experiments using synthetic data and on real fetal ultrasound images yield fused images of the whole fetus where, in average, β -VAE outperforms all the other methods in terms of preservation of patch information and overall image quality.
UR - http://www.scopus.com/inward/record.url?scp=85076214210&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33843-5_21
DO - 10.1007/978-3-030-33843-5_21
M3 - Conference contribution
AN - SCOPUS:85076214210
SN - 9783030338428
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 226
EP - 235
BT - Machine Learning for Medical Image Reconstruction - 2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Knoll, Florian
A2 - Maier, Andreas
A2 - Rueckert, Daniel
A2 - Ye, Jong Chul
PB - Springer
Y2 - 17 October 2019 through 17 October 2019
ER -