Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training
This incremental improvement addresses the challenge of vessel segmentation in medical imaging for liver disease analysis by reducing the need for large-scale annotated data.
The authors tackled the problem of segmenting liver vessels in non-contrast MRI by using a multi-task learning framework that leverages auxiliary contrast-enhanced data only during training, resulting in improved segmentation accuracy especially when few annotations are available.
Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodelling, associated with a wide spectrum of diffuse liver diseases. Existing approaches rely on contrast enhanced imaging data, but the necessary dedicated imaging sequences are not uniformly acquired. Images without contrast enhancement are acquired more frequently, but vessel segmentation is challenging, and requires large-scale annotated data. We propose a multi-task learning framework to segment vessels in liver MRI without contrast. It exploits auxiliary contrast enhanced MRI data available only during training to reduce the need for annotated training examples. Our approach draws on paired native and contrast enhanced data with and without vessel annotations for model training. Results show that auxiliary data improves the accuracy of vessel segmentation, even if they are not available during inference. The advantage is most pronounced if only few annotations are available for training, since the feature representation benefits from the shared task structure. A validation of this approach to augment a model for brain tumor segmentation confirms its benefits across different domains. An auxiliary informative imaging modality can augment expert annotations even if it is only available during training.