Adversarial Learning Framework for Cerebrovascular Landmark Detection Using Cross-Modality Information

Anatomical landmark detection plays an important role in cerebrovascular analysis and clinical treatment. However, due to the complex structure and similar local appearance around landmarks, the popular heatmap regression based methods suffer from the landmark confusion problem. In this work, we propose an adversarial learning framework for cerebrovascular landmark detection in MRA images by leveraging cross-modality information. Specifically, we exploit an unpaired large-scale CTA dataset to complement the limited MRA training data. The generator is modified as a U-Net based heatmap regression network, and the discriminator is trained using both MRA and CTA datasets to distinguish between multi-channel heatmap groundtruth and prediction. A relative coordinate matrix and a distance map are introduced to enhance landmark location distribution. Extensive experiments demonstrate the superior and robust performance of our method, even with very limited MRA training data.