Multi-Task Learning Network for Landmark Detection in Anatomical Tree Structures

Bifurcation landmark detection is an important step in automatic analysis of anatomical tree structures, such as airway and vessel. In this paper, we propose a multi-task learning based deep neural network specifically designed for automatically and accurately localizing bifurcation landmarks. Given an input volume, the network is trained to predict landmark confidence maps, branch segmentation maps, and branch orientation fields simultaneously. In this way, we exploit the spatial relationships among landmarks explicitly. The structural prior is also introduced to the architecture, which guides the network to learn more discriminative features. Experiments on airway tree and aorta tree demonstrate that the proposed method is effective for bifurcation landmark detection, and exploiting segmentation and orientation field regression as auxiliary objectives helps to increase accuracy of landmark detection substantially.