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Zimmer, V.A.* ; Gomez, A.* ; Skelton, E.* ; Toussaint, N.* ; Zhang, T.* ; Khanal, B.* ; Wright, R.* ; Noh, Y.* ; Ho, A.D.* ; Matthew, J.* ; Hajnal, J.V.* ; Schnabel, J.A.*

Towards whole placenta segmentation at late gestation using multi-view ultrasound images.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2019. 628-636 (Lect. Notes Comput. Sc. ; 11768 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11768 LNCS, Heft: , Seiten: 628-636 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)