Real-time prediction of segmentation quality

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Title: Real-time prediction of segmentation quality
Authors: Robinson, R
Oktay, O
Bai, W
Valindria, V
Sanghvi, MM
Aung, N
Paiva, JM
Zemrak, F
Fung, K
Lukaschuk, E
Lee, AM
Carapella, V
Kim, YJ
Kainz, B
Piechnik, SK
Neubauer, S
Petersen, SE
Page, C
Rueckert, D
Glocker, B
Item Type: Conference Paper
Abstract: Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image qual- ity, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accu- racy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a net- work to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE = 0.14 and 91% binary classifica- tion accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.
Issue Date: 13-Sep-2018
Date of Acceptance: 25-May-2018
URI: http://hdl.handle.net/10044/1/60750
DOI: 10.1007/978-3-030-00937-3_66
ISSN: 0302-9743
Publisher: Springer Verlag
Start Page: 578
End Page: 585
Journal / Book Title: Lecture Notes in Computer Science
Copyright Statement: © Springer Nature Switzerland AG 2018. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-00937-3_66
Sponsor/Funder: GlaxoSmithKline
Commission of the European Communities
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: H2020 - 757173
EP/P001009/1
Conference Name: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Keywords: Science & Technology
Technology
Computer Science, Theory & Methods
Imaging Science & Photographic Technology
Computer Science
cs.CV
cs.CV
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Finish Date: 2018-09-20
Conference Place: Granada, Spain
Online Publication Date: 2018-09-13
Appears in Collections:Faculty of Engineering
Computing
Department of Brain Sciences



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