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Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging

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Title: Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging
Authors: Meng, Q
Zimmer, V
Hou, B
Rajchl, M
Toussaint, N
Oktay, O
Schlemper, J
Gomez, A
Housden, J
Matthew, J
Rueckert, D
Schnabel, JA
Kainz, B
Item Type: Journal Article
Abstract: Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.
Issue Date: 1-Dec-2019
Date of Acceptance: 13-Apr-2019
URI: http://hdl.handle.net/10044/1/70220
DOI: 10.1109/TMI.2019.2913311
ISSN: 0278-0062
Publisher: IEEE
Start Page: 2755
End Page: 2767
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 38
Issue: 12
Copyright Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Wellcome Trust
Wellcome Trust/EPSRC
Wellcome Trust
Engineering & Physical Science Research Council (E
Nvidia
Engineering and Physical Sciences Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: RTJ5557761-1
PO :RTJ5557761-1
NS/A000025/1
RTJ5557761
RTJ5557761-1
Nvidia Hardware donation
EP/S013687/1
EP/S013687/1
EP/P001009/1
319456
Keywords: 08 Information and Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Article Number: 8698843
Online Publication Date: 2019-04-25
Appears in Collections:Computing
Faculty of Engineering