Realistic Adversarial Data Augmentation for MR Image Segmentation (2006.13322v1)

Abstract: Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.

A Critical Examination of Realistic Adversarial Data Augmentation for MR Image Segmentation

In recent developments within the domain of medical image analysis, neural networks have demonstrated significant potential in automating tasks such as segmentation. However, achieving robust performance often necessitates large, labeled datasets, the acquisition of which is cost-prohibitive and occasionally impractical due to privacy constraints. The paper presents a novel methodology employing realistic adversarial data augmentation to improve model generalization and robustness, particularly in the context of magnetic resonance (MR) image segmentation.

The authors propose a method that diverges from traditional adversarial attacks, which typically rely on pixel-wise noise generation. Instead, it simulates intensity inhomogeneities, a common source of artifacts in MR imaging, known as bias fields. This approach not only enhances model robustness against such real-world perturbations but also serves as a data augmentation strategy in both supervised and semi-supervised learning frameworks.

Methodological Insights

This research introduces an intensity transformation model that amplifies intensity non-uniformity to simulate bias field artifacts. The proposed adversarial method does not necessitate generative networks, thereby avoiding the associated complexities such as hyperparameter sensitivity and mode collapse. By employing a virtual adversarial training (VAT) strategy, the model continuously generates "hard" examples, thereby discouraging network overfitting. The paper extends the VAT approach by incorporating a composite distance function that includes a Kullback-Leibler divergence and a contour-based loss to detect boundary mismatches effectively.

Empirical Evaluation

The efficacy of the proposed adversarial data augmentation is demonstrated using the Automated Cardiac Diagnosis Challenge (ACDC) dataset, focusing on low-data scenarios. In these experiments, the method outperformed several baseline augmentation strategies, including VAT and Mixup, particularly in one-shot learning and cross-population settings. The paper showed that the use of adversarial bias fields yields Dice score improvements in both supervised (e.g., 0.650 with one labeled subject) and semi-supervised settings (e.g., 0.692 with one labeled subject). The results suggest that such augmentation strategies could significantly enhance model generalization capabilities, especially when training data is limited.

Discussion and Implications

The proposed method demonstrates potential for substantial implications in clinical settings where annotated medical data is scarce. By employing adversarial techniques that emulate real-world intensity perturbations, the research advances the robustness of neural network models in medical imaging. Practically, this could lead to more reliable automated systems that are resilient to common imaging artifacts, thereby augmenting diagnostic decisions, treatment planning, and clinical research applications.

Theoretically, this paper contributes to the understanding of adversarial training within the medical domain by providing alternative perturbation models that align more closely with actual imaging conditions. Future research could explore the application of these methods to other imaging modalities or anatomies, further generalizing the approach.

In conclusion, by circumventing the need for large datasets and reducing dependency on manually generated labels, the adversarial data augmentation strategy discussed in this paper marks a significant step towards the widespread adoption of AI in medical imaging. The approach has demonstrated robust improvements in task performance under challenging conditions, indicating its viability and utility in enhancing neural network-based medical image segmentation systems.