Unsupervised domain adaptation for medical imaging segmentation with self-ensembling (1811.06042v2)

Abstract: Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.

• The paper adapts the self-ensembling Mean Teacher model for unsupervised domain adaptation in medical imaging segmentation, specifically addressing performance drops caused by variability in data from different clinical centers.
• The authors demonstrate that leveraging unlabeled target domain data and exploring different consistency losses, particularly MSE, significantly improves segmentation accuracy and generalization.
• Empirical validation shows that the method improves prediction alignment across domains, indicating enhanced robustness for applying deep learning models in clinical settings with heterogeneous data.

Unsupervised Domain Adaptation for Medical Imaging Segmentation with Self-Ensembling

In the paper, the authors address the challenges of domain variability in medical imaging, particularly focusing on segmentation tasks using deep learning models. Such variability, characterized by inter-center differences in MRI acquisition parameters and protocols, often hampers generalization when models trained on data from one domain are applied to another. This paper introduces a self-ensembling method rooted in unsupervised domain adaptation to surmount these limitations and enhance semantic segmentation performance across disparate domains.

The authors extend the Mean Teacher model—a self-ensembling technique initially devised for semi-supervised learning—and adapt it for unsupervised domain adaptation in the field of medical imaging segmentation, utilizing unlabeled target domain data to improve model generalization. They employ a small yet realistic spinal cord gray matter MRI dataset to test and validate their approach, demonstrating that this methodology can yield better segmentation results even when leveraging a limited set of unlabeled data from target domains.

A characteristic limitation of many deep learning approaches on domain-shifted medical imaging data is the propensity to underperform due to the empirical risk minimization (ERM) principle. Models trained under ERM are typically optimized for source domains with assumptions that often do not hold across other domains. By employing self-ensembling, which utilizes both task and consistency losses, the authors circumvent this issue. Here, the Mean Teacher model is expanded beyond classification tasks to address the nuances of segmentation—a critical task in medical diagnostics.

The paper features several significant contributions:

1. Method Extension: The authors adapt the unsupervised domain adaptation approach of self-ensembling for segmentation tasks, marking a novel application in medical imaging.
2. Model Analysis: Different model components, including various consistency losses (MSE, Dice, and Cross-Entropy), are explored to determine the optimal configuration for segmentation tasks. The paper highlights that while cross-entropy was less stable, MSE demonstrated promising results.
3. Empirical Validation: Comprehensive evaluations reveal strong performance improvements attained by employing unlabeled data. The authors illustrate that enhancements were not solely attributable to the exponential moving average mechanism intrinsic to self-ensembling but indeed resulted from leveraging multi-domain unlabeled data.
4. Visualization and Analysis: Using t-SNE projections, the paper presents evidence that adaptation aligns predictions across domains, underscoring the reduction of discrepancies between the predictive spaces associated with different centers.

The implications of this paper are profound given the rapid adoption of deep learning in medical imaging. By demonstrating increased prediction alignment across varied MRI data domains, this work helps establish a more robust framework for deploying machine learning models in clinical settings characterized by heterogeneous data. Future developments might focus on refining this approach via more sophisticated consistency losses or incorporating adversarial training methodologies to further address domain adaptation challenges.

In conclusion, this paper advances the understanding of domain adaptation in medical imaging by representing a specific yet impactful application of self-ensembling methods. It challenges conventional supervised learning paradigms by emphasizing the value of unlabeled data and cross-domain evaluations. As segmentation remains a paramount task in medical diagnostics, these innovations could be pivotal in enhancing the accuracy and reliability of automated medical imaging analysis systems across various clinical environments.