Deep Learning to find signal abnormalities in medical images
Lead Research Organisation:
UNIVERSITY COLLEGE LONDON
Department Name: Medical Physics and Biomedical Eng
Abstract
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Publications
Shaw R
(2020)
A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal.
in IEEE transactions on medical imaging
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509577/1 | 30/09/2016 | 24/03/2022 | |||
| 1939674 | Studentship | EP/N509577/1 | 24/09/2017 | 25/12/2020 | Richard Shaw |
| Description | This work so far has focused on two main areas: 1) simulating motion artefacts in MRI and 2) automated quality control of MRI scans. 1) Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of clinical diagnosis and cause errors in automated image analysis. In this work, we have presented a method for generating realistic motion artefacts from artefact-free MRI data to be used in deep learning frameworks, increasing training appearance variability and ultimately making machine learning algorithms such as convolutional neural networks (CNNs) more robust to the presence of motion artefacts. By modelling patient movement as a sequence of randomly-generated rigid 3D transformations, we can generate motion synthetic artefact data. We show that by augmenting the training of segmentation CNNs with these artefacts, we can train models that generalise better and perform more reliably in the presence of artefact data, with negligible cost to their performance on clean data. We have shown that the performance of models trained using artefact data on segmentation tasks on real-world test-retest image pairs is more robust. We also demonstrated that our augmentation model can be used to learn to retrospectively remove certain types of motion artefacts from real MRI scans. Finally, we have shown that measures of uncertainty obtained from motion augmented CNN models reflect the presence of artefacts and can thus provide relevant information to ensure the safe usage of deep learning extracted biomarkers in a clinical pipeline. 2) Quality control (QC) of medical images is essential to ensure that downstream analysis such as segmentation can be performed successfully. Currently, QC is predominantly performed visually at significant time and operator cost. We have aimed to automate the process by formulating a probabilistic neural network that estimates uncertainty, hence providing a proxy measure of image quality that is learnt directly from the data. By augmenting the training data with different types of simulated artefacts, we have designed a novel neural network architecture based on a student-teacher framework to decouple sources of uncertainty related to different kinds of artefacts. This enables us to predict separate uncertainty quantities for the different types of data degradation. While the uncertainty measures reflect the presence and severity of image artefacts, the network also provides segmentation predictions that are as good as possible given the quality of the data. We have shown that models trained with simulated artefacts provide informative measures of uncertainty on real-world images and we validated our uncertainty predictions on problematic images identified by human-raters. |
| Exploitation Route | This work will hopefully enable safer deep learning systems for medical image analysis. |
| Sectors | Digital/Communication/Information Technologies (including Software) Healthcare Pharmaceuticals and Medical Biotechnology |