EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

File Description SizeFormat 
1807.10583v1.pdfWorking paper3.22 MBAdobe PDFView/Open
Title: EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Authors: 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
Item Type: Working Paper
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.
Issue Date: 1-Jan-2018
URI: http://hdl.handle.net/10044/1/62136
Copyright Statement: © 2018 The Author(s).
Sponsor/Funder: Engineering & Physical Science Research Council (E
Wellcome Trust
Wellcome Trust/EPSRC
Wellcome Trust
Engineering & Physical Science Research Council (E
Funder's Grant Number: RTJ5557761-1
PO :RTJ5557761-1
NS/A000025/1
RTJ5557761
RTJ5557761-1
Keywords: cs.CV
cs.LG
stat.ML
Notes: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 2018
Appears in Collections:Faculty of Engineering
Computing



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx