Contrastive Learning for View Classification of Echocardiograms

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naive baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations.

Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling

Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information. Furthermore, particular diseases, such as myocardial infarction, display irregular shapes on images and occupy small regions at random locations. These facts make pathology segmentation of multi-modal CMR images a challenging task. In this paper, we present the Max-Fusion U-Net that achieves improved pathology segmentation performance given aligned multi-modal images of LGE, T2-weighted, and bSSFP modalities. Specifically, modality-specific features are extracted by dedicated encoders. Then they are fused with the pixel-wise maximum operator. Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections. Furthermore, a spatial-attention module is applied in the last decoding layer to encourage the network to focus on those small semantically meaningful pathological regions that trigger relatively high responses by the network neurons. We also use a simple image patch extraction strategy to dynamically resample training examples with varying spacial and batch sizes. With limited GPU memory, this strategy reduces the imbalance of classes and forces the model to focus on regions around the interested pathology. It further improves segmentation accuracy and reduces the mis-classification of pathology. We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset which involves three modalities. Extensive experiments demonstrate the effectiveness of the proposed model which outperforms the related baselines.

Semi-supervised Pathology Segmentation with Disentangled Representations

Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation model that attempts to learn jointly for the first time: disentanglement of anatomy, modality, and pathology. The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations. In addition, a joint optimization strategy is proposed to fully take advantage of the available annotations. We evaluate our methods with two private cardiac infarction segmentation datasets with LGE-MRI scans. APD-Net can perform pathology segmentation with few annotations, maintain performance with different amounts of supervision, and outperform related deep learning methods.

Metrics for Exposing the Biases of Content-Style Disentanglement

Recent state-of-the-art semi- and un-supervised solutions for challenging computer vision tasks have used the idea of encoding image content into a spatial tensor and image appearance or "style" into a vector. These decomposed representations take advantage of equivariant properties of network design and improve performance in equivariant tasks, such as image-to-image translation. Most of these methods use the term "disentangled" for their representations and employ model design, learning objectives, and data biases to achieve good model performance. While considerable effort has been made to measure disentanglement in vector representations, currently, metrics that can characterize the degree of disentanglement between content (spatial) and style (vector) representations and the relation to task performance are lacking. In this paper, we propose metrics to measure how (un)correlated, biased, and informative the content and style representations are. In particular, we first identify key design choices and learning constraints on three popular models that employ content-style disentanglement and derive ablated versions. Then, we use our metrics to ascertain the role of each bias. Our experiments reveal a "sweet-spot" between disentanglement, task performance and latent space interpretability. The proposed metrics enable the design of better models and the selection of models that achieve the desired performance and disentanglement. Our metrics library is available at https://github.com/TsaftarisCollaboratory/CSDisentanglement_Metrics_Library.

Disentangled Representations for Domain-generalized Cardiac Segmentation

Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. Since the acquisition and annotation of medical data are costly and time-consuming, recent work focuses on domain adaptation and generalization to bridge the gap between data from different populations and scanners. In this paper, we propose two data augmentation methods that focus on improving the domain adaptation and generalization abilities of state-to-the-art cardiac segmentation models. In particular, our "Resolution Augmentation" method generates more diverse data by rescaling images to different resolutions within a range spanning different scanner protocols. Subsequently, our "Factor-based Augmentation" method generates more diverse data by projecting the original samples onto disentangled latent spaces, and combining the learned anatomy and modality factors from different domains. Our extensive experiments demonstrate the importance of efficient adaptation between seen and unseen domains, as well as model generalization ability, to robust cardiac image segmentation.

Pseudo-healthy synthesis with pathology disentanglement and adversarial learning

Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at \url{https://tobeprovided.upon.acceptance}

Learning to synthesise the ageing brain without longitudinal data

Brain ageing is a continuous process that is affected by many factors including neurodegenerative diseases. Understanding this process is of great value for both neuroscience research and clinical applications. However, revealing underlying mechanisms is challenging due to the lack of longitudinal data. In this paper, we propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable, and health state, i.e. status of Alzheimer's Disease (AD) for this work, as an ordinal variable. We adopt an adversarial loss to learn the joint distribution of brain appearance and clinical variables and define reconstruction losses that help preserve subject identity. To demonstrate our model, we compare with several approaches using two widely used datasets: Cam-CAN and ADNI. We use ground-truth longitudinal data from ADNI to evaluate the quality of synthesised images. A pre-trained age predictor, which estimates the apparent age of a brain image, is used to assess age accuracy. In addition, we show that we can train the model on Cam-CAN data and evaluate on the longitudinal data from ADNI, indicating the generalisation power of our approach. Both qualitative and quantitative results show that our method can progressively simulate the ageing process by synthesising realistic brain images. The code will be made publicly available at: https://github.com/xiat0616/BrainAgeing.

Disentangle, align and fuse for multimodal and zero-shot image segmentation

Magnetic resonance (MR) protocols rely on several sequences to properly assess pathology and organ status. Yet, despite advances in image analysis we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the information shared between modalities (largely an organ's anatomy) is beneficial for multi-modality multi-input processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to claim this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, enabling semi-supervised and zero shot learning. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, that non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data, and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation.