Multi-input and dataset-invariant adversarial learning (MDAL) for left and right-ventricular coverage estimation in cardiac MRI

Cardiac functional parameters, such as, the Ejection Fraction (EF) and Cardiac Output (CO) of both ventricles, are most immediate indicators of normal/abnormal cardiac function. To compute these parameters, accurate measurement of ventricular volumes at end-diastole (ED) and end-systole (ES) are required. Accurate volume measurements depend on the correct identification of basal and apical slices in cardiac magnetic resonance (CMR) sequences that provide full coverage of both left (LV) and right (RV) ventricles. This paper proposes a novel adversarial learning (AL) approach based on convolutional neural networks (CNN) that detects and localizes the basal/apical slices in an image volume independently of image-acquisition parameters, such as, imaging device, magnetic field strength, variations in protocol execution, etc. The proposed model is trained on multiple cohorts of different provenance, and learns image features from different MRI viewing planes to learn the appearance and predict the position of the basal and apical planes. To the best of our knowledge, this is the first work tackling the fully automatic detection and position regression of basal/apical slices in CMR volumes in a dataset-invariant manner. We achieve this by maximizing the ability of a CNN to regress the position of basal/apical slices within a single dataset, while minimizing the ability of a classifier to discriminate image features between different data sources. Our results show superior performance over state-of-the-art methods

Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy. To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis. Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset

Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Accurate ventricular volume measurements depend on complete heart coverage in cardiac magnetic resonance (CMR) from where most immediate indicators of normal/abnormal cardiac function are available non-invasively. However, incomplete coverage, especially missing basal or apical slices in CMR sequences is insufficiently addressed in population imaging and current clinical research studies yet has important impact on volume calculation accuracy. In this work, we propose a new deep architecture, coined Missing Slice Imputation Generative Adversarial Network (MSIGAN), to learn key features of cardiac short-axis (SAX) slices across different positions, and use them as conditional variables to effectively infer missing slices in the query volumes. In MSIGAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold conditional on slice intensity through a generator net. The latent vector along with the slice features (i.e., intensity) and desired position control the generation vs. regression. Two adversarial networks are imposed on the regressor and generator, encouraging more realistic slices. Experimental results show that our method outperforms the previous state-of-the-art in missing slice imputation for cardiac MRI

Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains

Population imaging studies rely upon good quality medical imagery before downstream image quantification. This study provides an automated approach to assess image quality from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts, including respiratory motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with images acquired in different views, including two, three, and four-chamber long-axis and short-axis cine CMR images. Two deep learning-based models in spatial and frequency domains are proposed. Besides recognising these artefacts, the proposed models are suitable to the common challenges of not having access to data labels. An unsupervised domain adaptation method and a Fourier-based convolutional neural network are proposed to overcome these challenges. We show that the proposed models reliably allow for CMR image quality assessment. The accuracies obtained for the spatial model in supervised and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank dataset, respectively. Using unsupervised domain adaptation can somewhat overcome the challenge of not having access to the data labels. The maximum achieved domain gap coverage in unsupervised domain adaptation is 16.86%. Domain adaptation can significantly improve a 5-class classification task and deal with considerable domain shift without data labels. Increasing the speed of training and testing can be achieved with the proposed model in the frequency domain. The frequency-domain model can achieve the same accuracy yet 1.548 times faster than the spatial model. This model can also be used directly on k-space data, and there is no need for image reconstruction.Comment: 21 pages, 9 figures, 7 table

A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images

Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are among the things that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. Methods: The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. Results: The results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. Conclusion: The proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts.Comment: 16 pages, 1 figure, 2 table

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging