Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available.

Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization

Deep learning-based image registration approaches have shown competitive performance and run-time advantages compared to conventional image registration methods. However, existing learning-based approaches mostly require to train separate models with respect to different regularization hyperparameters for manual hyperparameter searching and often do not allow spatially-variant regularization. In this work, we propose a learning-based registration approach based on a novel conditional spatially adaptive instance normalization (CSAIN) to address these challenges. The proposed method introduces a spatially-variant regularization and learns its effect of achieving spatially-adaptive regularization by conditioning the registration network on the hyperparameter matrix via CSAIN. This allows varying of spatially adaptive regularization at inference to obtain multiple plausible deformations with a single pre-trained model. Additionally, the proposed method enables automatic hyperparameter optimization to avoid manual hyperparameter searching. Experiments show that our proposed method outperforms the baseline approaches while achieving spatially-variant and adaptive regularization.

The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)

The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-world respiratory motion artifacts is unclear. This manuscript describes the design of extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion Challenge). The challenge aims to establish a public benchmark dataset to assess the effects of respiratory motion on image quality and examine the robustness of segmentation models. The challenge recruited 40 healthy volunteers to perform different breath-hold behaviors during one imaging visit, obtaining paired cine imaging with artifacts. Radiologists assessed the image quality and annotated the level of respiratory motion artifacts. For those images with diagnostic quality, radiologists further segmented the left ventricle, left ventricle myocardium and right ventricle. The images of training set (20 volunteers) along with the annotations are released to the challenge participants, to develop an automated image quality assessment model (Task 1) and an automated segmentation model (Task 2). The images of validation set (5 volunteers) are released to the challenge participants but the annotations are withheld for online evaluation of submitted predictions. Both the images and annotations of the test set (15 volunteers) were withheld and only used for offline evaluation of submitted containerized dockers. The image quality assessment task is quantitatively evaluated by the Cohen's kappa statistics and the segmentation task is evaluated by the Dice scores and Hausdorff distances.

Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance

Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease diagnosis and treatment. Particularly, in recent years, there has been a significant growth in the use of AI and ML algorithms, especially Deep Learning (DL) based methods, for medical image reconstruction. DL techniques have shown to be competitive and often superior over conventional reconstruction methods in terms of both reconstruction quality and computational efficiency. The use of DL-based image reconstruction also provides promising opportunities to transform the way cardiac images are acquired and reconstructed. In this chapter, we will review recent advances in DL-based reconstruction techniques for cardiac imaging, with emphasis on cardiac magnetic resonance (CMR) image reconstruction. We mainly focus on supervised DL methods for the application, including image post-processing techniques, model-driven approaches and k-space based methods. Current limitations, challenges and future opportunities of DL for cardiac image reconstruction are also discussed.

MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.

Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior

Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method leverages a variational autoencoder-based generative model to learn a manifold for biomechanically plausible deformations. The motion tracking then can be performed via traversing the learnt manifold to search for the optimal transformations while considering the sequence information. The proposed method is validated on three public cardiac cine MRI datasets with comprehensive evaluations. The results demonstrate that the proposed method can outperform other approaches, yielding higher motion tracking accuracy with reasonable volume preservation and better generalizability to varying data distributions. It also enables better estimates of myocardial strains, which indicates the potential of the method in characterizing spatiotemporal signatures for understanding cardiovascular diseases.

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Data augmentation has been widely used in deep learning to reduce over-fitting and improve the robustness of models. However, traditional data augmentation techniques, e.g., rotation, cropping, flipping, etc., do not consider \textit{semantic} transformations, e.g., changing the age of a brain image. Previous works tried to achieve semantic augmentation by generating \textit{counterfactuals}, but they focused on how to train deep generative models and randomly created counterfactuals with the generative models without considering which counterfactuals are most \textit{effective} for improving downstream training. Different from these approaches, in this work, we propose a novel adversarial counterfactual augmentation scheme that aims to find the most \textit{effective} counterfactuals to improve downstream tasks with a pre-trained generative model. Specifically, we construct an adversarial game where we update the input \textit{conditional factor} of the generator and the downstream \textit{classifier} with gradient backpropagation alternatively and iteratively. The key idea is to find conditional factors that can result in \textit{hard} counterfactuals for the classifier. This can be viewed as finding the `\textit{weakness}' of the classifier and purposely forcing it to \textit{overcome} its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as the downstream task based on a pre-trained brain ageing synthesis model. We show the proposed approach improves test accuracy and can alleviate spurious correlations. Code will be released upon acceptance.

Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU

Physiological monitoring in intensive care units generates data that can be used to aid clinical decision making facilitating early interventions. However, the low data quality of physiological signals due to the recording conditions in clinical settings limits the automated extraction of relevant information and leads to significant numbers of false alarms. This paper investigates the utilization of a hybrid artifact detection system that combines a Variational Autoencoder with a statistical detection component for the labeling of artifactual samples to automate the costly process of cleaning physiological recordings. The system is applied to mean blood pressure signals from an intensive care unit dataset recorded within the scope of the KidsBrainIT project. Its performance is benchmarked to manual annotations made by trained researchers. Our preliminary results indicate that the system is capable of consistently achieving sensitivity and specificity levels that surpass 90%. Thus, it provides an initial foundation that can be expanded upon to partially automate data cleaning in offline applications and reduce false alarms in online applications.

GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization for Deformable Image Registration

We present a Gradient Descent-based Image Registration Network (GraDIRN) for learning deformable image registration by embedding gradient-based iterative energy minimization in a deep learning framework. Traditional image registration algorithms typically use iterative energy-minimization optimization to find the optimal transformation between a pair of images, which is time-consuming when many iterations are needed. In contrast, recent learning-based methods amortize this costly iterative optimization by training deep neural networks so that registration of one pair of images can be achieved by fast network forward pass after training. Motivated by successes in image reconstruction techniques that combine deep learning with the mathematical structure of iterative variational energy optimization, we formulate a novel registration network based on multi-resolution gradient descent energy minimization. The forward pass of the network takes explicit image dissimilarity gradient steps and generalized regularization steps parameterized by Convolutional Neural Networks (CNN) for a fixed number of iterations. We use auto-differentiation to derive the forward computational graph for the explicit image dissimilarity gradient w.r.t. the transformation, so arbitrary image dissimilarity metrics and transformation models can be used without complex and error-prone gradient derivations. We demonstrate that this approach achieves state-of-the-art registration performance while using fewer learnable parameters through extensive evaluations on registration tasks using 2D cardiac MR images and 3D brain MR images.

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

Deep learning models usually suffer from domain shift issues, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. Under this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation tasks: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-center prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.