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Khanal, B.* ; Gomez, A.* ; Toussaint, N.* ; McDonagh, S.* ; Zimmer, V.* ; Skelton, E.* ; Matthew, J.* ; Grzech, D.* ; Wright, R.* ; Gupta, C.* ; Hou, B.* ; Rueckert, D.* ; Schnabel, J.A.* ; Kainz, B.*

EchoFusion: Tracking and reconstruction of objects in 4D freehand ultrasound imaging without external trackers.

In: (International Workshop on Preterm, Perinatal and Paediatric Image Analysis). Berlin [u.a.]: Springer, 2018. 117-127 (Lect. Notes Comput. Sc. ; 11076 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Preterm, Perinatal and Paediatric Image Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11076 LNCS, Heft: , Seiten: 117-127 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)