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Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided brachytherapy. However, most current studies are concentrated on single-needle detection by only using a small number of images with a needle, regardless of the massive database of US images without needles. In this paper, we propose a workflow for multi-needle detection by considering the images without needles as auxiliary. Concretely, we train position-specific dictionaries on 3D overlapping patches of auxiliary images, where we develop an enhanced sparse dictionary learning method by integrating spatial continuity of 3D US, dubbed order-graph regularized dictionary learning. Using the learned dictionaries, target images are reconstructed to obtain residual pixels which are then clustered in every slice to yield centers. With the obtained centers, regions of interest …