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Real-Time Prediction of Segmentation Quality

Robinson R, Oktay O, Bai W, Valindria VV, 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 (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11073 LNCS

Pages Range: 578-585

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030009366

DOI: 10.1007/978-3-030-00937-3_66

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 quality, 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 accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE = 0.14 and 91% binary classification 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.

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How to cite

APA:

Robinson, R., Oktay, O., Bai, W., Valindria, V.V., Sanghvi, M.M., Aung, N.,... Glocker, B. (2018). Real-Time Prediction of Segmentation Quality. In Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 578-585). Granada, ESP: Springer Verlag.

MLA:

Robinson, Robert, et al. "Real-Time Prediction of Segmentation Quality." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos, Springer Verlag, 2018. 578-585.

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