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

Bishesh Khanal*, Alberto Gomez, Nicolas Toussaint, Steven McDonagh, Veronika Zimmer, Emily Skelton, Jacqueline Matthew, Daniel Grzech, Robert Wright, Chandni Gupta, Benjamin Hou, Daniel Rueckert, Julia A. Schnabel, Bernhard Kainz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationData Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis - First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018 Held in Conjunction with MICCAI 2018, Proceedings
PublisherSpringer Verlag
Pages117-127
Number of pages11
ISBN (Print)9783030008062
DOIs
Publication statusPublished - 1 Jan 2018
Event1st 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, Spain
Duration: 16 Sept 201816 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11076 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st 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
Country/TerritorySpain
CityGranada
Period16/09/201816/09/2018

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