- The paper proposes UMCT, a framework that integrates multi-view co-training with uncertainty estimation to generate reliable pseudo labels for unlabeled data.
- It employs spatial transformations and 3D deep network variants initialized with 2D pre-trained weights to enhance feature diversity in volumetric image segmentation.
- Extensive experiments validate UMCT’s superiority over state-of-the-art methods, improving both semi-supervised segmentation and domain adaptation in challenging datasets.
Uncertainty-aware Multi-view Co-training for Semi-supervised Medical Image Segmentation and Domain Adaptation
Medical image segmentation is a crucial task in the field of medical image analysis, supporting various clinical applications such as diagnosis and surgical planning. Given the high cost of obtaining labeled medical image data, semi-supervised learning (SSL) and unsupervised domain adaptation (UDA) are often utilized to leverage unlabeled data effectively. This paper introduces a novel framework, Uncertainty-aware Multi-view Co-training (UMCT), aimed at addressing challenges in SSL and UDA for volumetric medical images.
Overview of Proposed Framework
The UMCT framework introduces a multi-view co-training strategy that utilizes multiple views of 3D data generated through spatial transformations such as rotation and permutation. Each view is processed by a 3D deep network variant designed to encourage diverse feature learning through asymmetric convolution kernels initialized with 2D pre-trained weights.
One of the paper's technical highlights is the incorporation of uncertainty estimation into the co-training process. By transforming model outputs into Bayesian random variables via dropout layers, epistemic uncertainty is estimated for each view’s predictions. This provides a measure of the confidence associated with each view's predictions on unlabeled data. The framework employs these uncertainty estimates to generate reliable pseudo labels for co-training, enhancing segmentation performance on unlabeled datasets.
Experimental Validation
In extensive experiments, the UMCT framework demonstrates strong performance in semi-supervised segmentation on the NIH pancreas dataset and a multi-organ ABDCT dataset. Compared to state-of-the-art methods such as deep co-training and transformation-consistent self-ensembling, UMCT achieves higher segmentation accuracy, particularly under limited labeled data settings. Notably, UMCT maintains superior performance across various percentages of labeled data, showcasing its efficiency in utilizing unlabeled data to reduce labeling efforts.
The framework also addresses UDA challenges, achieving significant improvements in domain adaptation without requiring access to the source domain data—an often encountered real-world scenario. This is validated through experiments on the Medical Segmentation Decathlon datasets, where models trained on multi-organ data are adapted to pancreas and liver datasets with substantial distribution shifts.
Implications and Future Directions
By effectively handling both SSL and UDA tasks, UMCT contributes towards making deep learning models more robust and generalizable across datasets with minimal annotated data. The ability to handle domain shifts without source domain data is particularly promising for practical deployments where access to comprehensive datasets is restricted.
Future work could explore expanding the scope of view transformations to enhance model robustness further, as well as investigating co-training strategies in the context of modality adaptation (e.g., CT to MRI). Continued advancements in these directions will likely deepen the integration of UMCT approaches into clinical workflows by improving the adaptability and performance of segmentation models in diverse medical imaging contexts.
In summary, the paper provides a cohesive framework for uncertainty-aware multi-view co-training, significantly advancing the potential of semi-supervised and domain adaptation techniques in medical image analysis.