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

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 JA, Kainz B (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11076 LNCS

Pages Range: 117-127

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030008062

DOI: 10.1007/978-3-030-00807-9_12

Abstract

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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How to cite

APA:

Khanal, B., Gomez, A., Toussaint, N., Mcdonagh, S., Zimmer, V., Skelton, E.,... Kainz, B. (2018). EchoFusion: Tracking and reconstruction of objects in 4D freehand ultrasound imaging without external trackers. In Andrew Melbourne, Rosalind Aughwane, Emma Robinson, Roxane Licandro, Melanie Gau, Martin Kampel, Matthew DiFranco, Paolo Rota, Roxane Licandro, Pim Moeskops, Ernst Schwartz, Antonios Makropoulos (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 117-127). Granada, ESP: Springer Verlag.

MLA:

Khanal, Bishesh, et al. "EchoFusion: Tracking and reconstruction of objects in 4D freehand ultrasound imaging without external trackers." Proceedings of the 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Andrew Melbourne, Rosalind Aughwane, Emma Robinson, Roxane Licandro, Melanie Gau, Martin Kampel, Matthew DiFranco, Paolo Rota, Roxane Licandro, Pim Moeskops, Ernst Schwartz, Antonios Makropoulos, Springer Verlag, 2018. 117-127.

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