- The paper introduces an unsupervised adversarial framework that minimizes domain discrepancies in brain lesion segmentation.
- It employs a multi-connected CNN and discriminator to align features from diverse MR imaging protocols without target labels.
- Experiments show a 3% improvement in DSC, narrowing the gap with supervised adaptation methods for TBI MR scans.
Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks
The presented paper addresses a significant challenge in biomedical image analysis: the degradation of segmentation performance when applying machine learning models across varying domains due to differences in imaging protocols. Specifically, the authors explore unsupervised domain adaptation (UDA) for brain lesion segmentation by utilizing adversarial neural networks. This approach seeks to train a segmentation model that remains invariant to differences in input data without requiring labels from the test domain.
Methodology
The core methodology harnesses adversarial neural networks to achieve domain adaptation. The paper specifically innovates within the context of brain lesion segmentation by adapting segmenter networks using domain-discriminative feedback. The primary architecture comprises a fully convolutional neural network (CNN) segmenter and a domain discriminator. The discriminator functions as an auxiliary network tasked with classifying the domain based on hidden layer activations from the segmenter. The aim is to encourage the segmenter to learn features that are indistinguishable between domains by maximizing the domain classification error during training.
A key contribution of the paper is the introduction of multi-connected adversarial networks. By connecting the domain discriminator to multiple depths within the segmenter, the system mitigates challenges associated with feature adaptation, thereby improving domain agnosticity across learned representations. This multi-layer connection is argued to enhance adversarial training by providing more diverse adversarial gradients and refining feature learning deeper within the networks.
Experimental Setup
The research utilizes two important datasets of MR brain scans from traumatic brain injury (TBI) patients. One dataset includes Gradient Echo (GE), while the other utilizes Susceptibility Weighted Imaging (SWI), with both datasets differing in their imaging sequences, leading to the need for domain adaptation. The experimentation focuses on transferring knowledge from the GE-inclusive source domain to the SWI-inclusive target domain without having access to target domain annotations.
Results
The experimentation demonstrates that the proposed UDA approach reduces the segmentation accuracy gap between non-adaptive models trained only on source data and those employing supervised adaptation with target domain labels. Specifically, the method achieves segmentation accuracies close to the upper bound established by supervised domain adaptation techniques. By integrating domain adaptation, the approach shows a 3% improvement in Dice Similarity Coefficient (DSC) over non-adaptive counterparts, accounting for a substantial portion of the performance gap.
Implications and Future Work
The paper's findings have significant implications for multi-center studies where imaging protocol variations are prevalent. By reducing reliance on labor-intensive manual annotation in new domains, this method promises to enhance the scalability and applicability of machine learning models in medical imaging.
For future work, the authors propose extending their approach to evaluate its efficacy across various databases with different domain variations. They also suggest exploring alternative domain adaptation methods, such as those based on the minimization of maximum mean discrepancy.
In summary, this paper contributes to the field of domain adaptation in image segmentation by providing a robust adversarial training approach that effectively mitigates domain-specific discrepancies, offering both practical and theoretical advancements in automated medical image analysis.