Standard plane detection in 3D fetal ultrasound using an iterative transformation network

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Title: Standard plane detection in 3D fetal ultrasound using an iterative transformation network
Authors: Li, Y
Khanal, B
Hou, B
Alansary, A
Cerrolaza, J
Sinclair, M
Matthew, J
Gupta, C
Knight, C
Kainz, B
Rueckert, D
Item Type: Conference Paper
Abstract: Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for the transventricular and transcerebellar planes respectively and takes 0.46s per plane.
Issue Date: 31-Dec-2018
Date of Acceptance: 25-May-2018
URI: http://hdl.handle.net/10044/1/60751
ISSN: 0302-9743
Publisher: Springer Verlag
Journal / Book Title: Lecture Notes in Computer Science
Copyright Statement: This paper is embargoed until publication.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Wellcome Trust
Wellcome Trust/EPSRC
Wellcome Trust
Engineering & Physical Science Research Council (E
Nvidia
Funder's Grant Number: RTJ5557761-1
PO :RTJ5557761-1
NS/A000025/1
RTJ5557761
RTJ5557761-1
Nvidia Hardware donation
Conference Name: 21st International Conference on Medical Image Computing and Computer Assisted Intervention
Keywords: cs.CV
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Accepted
Start Date: 2018-09-16
Finish Date: 2018-09-20
Conference Place: Granada, Spain
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Engineering
Computing



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