Reliable Machine Learning for Medical Imaging Data Through Automated Quality Control and Data Harmonization

Reliable Machine Learning for Medical Imaging Data through Automated Quality Control and Data Harmonization. 1 Author Robert Robinson Supervisors Dr. Ben Glocker 2 Submitted May 22nd 2020 Dr. Chris Page 2 Dr. Marius de Groot 1 Prof. Daniel Rueckert 1 BioMedIA, Department of Computing, Imperial College London, UK. 2 GlaxoSmithKline Research and Development, Uxbridge, UK. A thesis submitted in fulfilment of the requirements for the degree of Philosophiae Doctor Biomedical Image Analysis Group, Department of Computing, Imperial College London. BioMedIA, Department of Computing, Huxley Building, Imperial College London, 180 Queens Gate, South Kensington, SW7 2AZ Robert D. Robinson © 2020 A learning experience is one of those things that says, ‘You know that thing you just did? Don’t do that.’ Douglas Adams - The Salmon of Doubt Many learning experiences occurred conducting this research. For all of their valuable support, this thesis, the fruit of these last 5 years, is dedicated to my partner, Nick, my parents, Margie and Eric, and my friends. It is also dedicated to Danny, my constant companion. I would never have lived to begin, much less finish, the work herein if it weren’t for my Dad; he is a true life-saver and superhero. Declaration The works presented in this thesis are original ideas fostered jointly between the author and their supervisors. To the best of the author’s knowledge, the work is original and attribution has been given where appropriate. Where others have contributed to the work, it has been acknowledged in co-authorship of the relevant papers outlined here. The work in chapter3 was published in the conference proceedings of the 20 th international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2017. The work in chapter4 was published in the conference proceedings of the 21 st international conference on MICCAI in 2018. The work from chapter3 was extended incorporating work from chapter4 into the article published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2019. The work in chapter5 is submitted to the 23 rd international conference on MICCAI in 2020. The work in this thesis has been presented as an abstract in poster and oral form at various conferences and events. This document was written solely by the author unless otherwise acknowledged. All figures and tables have been generated by the author. The document has been formatted and styled by the author. Some illustrations, generated by the author, have been previously uploaded to their personal web- site at https://mlnotebook.github.io under a Creative Commons Attribution-Non Commercial 4.0 International Licence. This thesis has been submitted in fulfilment of the requirements for the degree of Philosophiae Doc- tor at Imperial College London only. It has not been submitted for any other degree or professional qualification. 6 Copyright The copyright of this thesis rests with the author. Unless otherwise indicated, its contents are licensed under a Creative Commons Attribution-Non Commercial 4.0 International Licence (CC BY-NC). Under this licence, you may copy and redistribute the material in any medium or format. You may also create and distribute modified versions of the work. This is on the condition that: you credit the author and do not use it, or any derivative works, for a commercial purpose. When reusing or sharing this work, ensure you make the licence terms clear to others by naming the licence and linking to the licence text. Where a work has been adapted, you should indicate that the work has been changed and describe those changes. Please seek permission from the copyright holder for uses of this work that are not included in this licence or permitted under UK Copyright Law. 7 Acknowledgements The contributions of this thesis were funded by the Engineering and Physical Science Research Council (EPSRC) Centre for Doctoral Training (CDT) in Medical Imaging (EP/L015226/1), jointly hosted between King’s College, London, and Imperial College, London. The work was sponsored by GlaxoSmithKline (GSK), an industrial partner to the CDT. All contributions were authored at the Biomedical Image Analysis Group (BioMedIA) group in the Department of Computing at Imperial College London. The research was conducted under the supervision of Dr. Ben Glocker, Reader in Machine Learning for Medical Imaging from BioMedIA at Imperial College London. Secondary supervision was conducted by Prof. Daniel Rueckert, Head of Computing at Imperial college London. Industrial supervision was provided by Dr. Chris Page and Dr. Marius de Groot from GSK. This work would not have been possible without access to the data provided by the UK Biobank (UKBB) imaging study under Access Applications 18545, 12579 and 2964, nor the manual con- tours of Cardiac Magnetic Resonance Imaging (CMR) scans contributed by Access Application 2964. The manual Quality Control (QC) scores contributed by H. Suzuki are also thankfully acknowledged. The credit for the development of Reverse Classification Accuracy (RCA) lies firmly with Dr. V. Valindria et al. [164]. Credit for the base Image and Spatial Transformer Network (ISTN) framework belongs to Dr. M. Lee et al. [99]. 8 Abstract In medical image analysis pipelines, Machine Learning (ML) is increasingly employed to process images and to derive clinically relevant biomarkers and quantitative measures. Results of these analyses are used to inform clinical decision-making from diagnosis to treatment planning. It is imperative thatML models work reliably, and their results are adequately scrutinized to avoid introducing bias or inaccuracy into downstream tasks. Image segmentation is a key component of many medical image analysis pipeline. For a segmentor to be reliable, it must be possible to identify instances when it has failed. This work presents an extensive validation of an approach for automated Quality Con- trol (QC) of Cardiac Magnetic Resonance Imaging (CMR) segmentations using a reverse testing strategy, which is modified from Reverse Classification Accuracy (RCA)[164]. The method is evaluated on data from the UKBB imaging study, demonstrating its ef- fectiveness at large scale in the absence of Ground Truth (GT). It achieves over 95% accuracy classifying segmentation quality using Dice Similarity Coefficient (DSC). It may be desirable to obtain an immediate assessment of segmentation quality in high- throughput pipelines, in pharmaceutical trials, or in the clinic to inform operators when acquired images yield poor quality analysis results. This study presents a method for real-time automatedQC using Deep Learning (DL)-based frameworks which directly pre- dict DSCs and proxy RCA scores, achieving MAE = 0.03 and MAE = 0.14 respectively. A model may fail because it is evaluated on data drawn from a different distribution to that on which it was trained, as with scans acquired from different sites. This work intro- duces and evaluates an unpaired, unsupervised method for Domain Adaptation (DA) on multi-site neuroimaging data, aiming to mitigate the degradation in model performance due to Domain Shift (DS) caused by population and acquisition changes. Over 20% performance is recovered in a classification task when applying constrained appearance and spatial transformations with Image and Spatial Transformer Networks (ISTNs). 10 Contents 1 Introduction 24 1.1 Overview...................................... 25 1.2 Contributions.................................... 27 2 Background 30 2.1 Overview...................................... 31 2.2 Predictive Modelling................................ 32 2.2.1 Introduction................................ 32 2.2.2 Machine Learning............................. 33 2.2.2.1 K-means Clustering Method................... 34 2.2.2.2 Support Vector Machine Classification Method......... 35 2.2.2.3 Decision Tree Methods..................... 36 2.2.2.4 Deep-learning Methods..................... 39 2.2.3 Deep Learning............................... 40 2.2.3.1 Convolutional Neural Networks................. 44 2.2.3.2 Generative Adversarial Networks................ 49 2.2.4 Image-to-Image Translation........................ 49 2.2.4.1 Training Image-to-Image Translation Networks......... 50 2.2.5 Model Evaluation.............................. 51 2.2.5.1 Confusion Matrix........................ 52 2.2.5.2 Logarithmic Loss........................ 53 2.2.5.3 Area Under Curves....................... 54 2.3 Image Segmentation................................ 56 2.3.1 Introduction................................ 56 2.3.2 Traditional Image Segmentation Methods................. 56 2.3.2.1 Intensity-based Segmentation Methods............. 56 2.3.2.2 Region-based Segmentation Methods............. 58 2.3.2.3 Edge-based Segmentation Methods............... 59 2.3.2.4 Curve-evolution Segmentation Methods............ 60 2.3.2.5 Graph-based Segmentation Methods.............. 62 2.3.3 Registration-based Image Segmentation Methods............. 64 2.3.4 Machine Learning-based Image Segmentation............... 66 2.4 Image Registration................................. 69 12 Contents Contents 2.4.1 Introduction................................ 69 2.4.1.1 Image Registration Overview.................. 69 2.4.1.2 Feature Extraction........................ 70 2.4.1.3 Feature Correspondence.................... 71 2.4.1.4 Transformation Estimation................... 77 2.4.1.5 Resampling........................... 82 2.5 Challenges to ML Deployment in Medical Imaging................. 84 2.5.1

Load more