PuSH - Publikationsserver des Helmholtz Zentrums München

Gomez, A.* ; Zimmer, V.* ; Toussaint, N.* ; Wright, R.* ; Clough, J.R.* ; Khanal, B.* ; van Poppel, M.P.M.* ; Skelton, E.* ; Matthews, J.* ; Schnabel, J.A.*

Image reconstruction in a manifold of image patches: Application to whole-fetus ultrasound imaging.

In: (International Workshop on Machine Learning for Medical Image Reconstruction). Berlin [u.a.]: Springer, 2019. 226-235 (Lect. Notes Comput. Sc. ; 11905 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Machine Learning for Medical Image Reconstruction
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11905 LNCS, Heft: , Seiten: 226-235 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)