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.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings|
|Editors||Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos|
|Number of pages||8|
|Publication status||Published - 2018|
|Event||21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain|
Duration: 2018 Sep 16 → 2018 Sep 20
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018|
|Period||18/9/16 → 18/9/20|
Bibliographical noteFunding Information:
Acknowledgements. RR is funded by KCL&Imperial EPSRC CDT in Medical Imaging (EP/L015226/1) and GlaxoSmithKline; VV by Indonesia Endowment for Education (LPDP) Indonesian Presidential PhD Scholarship; KF supported by The Medical College of Saint Bartholomew’s Hospital Trust. AL and SEP acknowledge support from NIHR Barts Biomedical Research Centre and EPSRC program grant (EP/P001009/ 1). SN and SKP are supported by the Oxford NIHR BRC and the Oxford British Heart Foundation Centre of Research Excellence. This project supported by the MRC (grant number MR/L016311/1). NA is supported by a Wellcome Trust Research Training Fellowship (203553/Z/Z). The authors SEP, SN and SKP acknowledge the British Heart Foundation (BHF) (PG/14/89/31194). BG received funding from the ERC under Horizon 2020 (grant agreement No. 757173, project MIRA, ERC-2017-STG).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)