Individual tree extraction from urban mobile laser scanning (MLS) point clouds is important for many urban applications. Recently, deep learning-based semantic segmentation of urban MLS point clouds has achieved significant progress, which makes it possible to segment tree point clouds. However, tree segments often are spatially overlapping with varying shapes and incompleteness caused by occlusion, which makes individual tree extraction a challenging task. In this paper, we propose a novel top-down approach to extract individual trees from urban MLS point clouds. Firstly, a semantic segmentation deep network is applied to segment tree points from raw urban MLS point clouds, and then the segmented tree points are further grouped into a set of tree clusters using Euclidean distance clustering. Next, a pointwise direction embedding deep network (PDE-net) is proposed to predict the direction vectors pointing to tree centers for each tree cluster to enhance the boundaries of instance-level trees. After that, a direction aggregation-based strategy is developed to detect the tree centers for each tree cluster, and the clusters are classified into single-tree clusters and multi-tree clusters based on the number of detected tree centers. Finally, the single-tree clusters are directly extracted as individual trees, while the multi-tree clusters are further separated into instance-level trees based on our proposed accessible region growing algorithm combining the embedded pointwise directions and detected tree centers. Four MLS point clouds collected from different urban scenes were used to evaluate the performance of the proposed method. The precision, recall, and F-score of 0.96, 0.94, and 0.95, respectively, on these four datasets demonstrate the effectiveness of our approach. An implementation of the proposed method is available at: https://github.com/HiphonL/IndividualTreeExtraction.