Multi-needle detection in 3D ultrasound images with sparse dictionary learning

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 of multi-needle detection via considering the images without needles as auxiliary. Specifically, we train position-specific dictionaries on 3D overlapping patches of auxiliary images, where we developed an enhanced sparse dictionary learning method by integrating spatial continuity of 3D US, dubbed order-graph regularized dictionary learning (ORDL). Using the learned dictionaries, target images are reconstructed to obtain residual pixels which are then clustered in every slice to determine the centers. With the obtained centers, regions of interest (ROIs) are constructed via seeking cylinders. Finally, we detect needles by using the random sample consensus algorithm (RANSAC) per ROI and then locate the tips by finding the sharp intensity drops along the detected axis for every needle. Extensive experiments are conducted on a prostate data set of 70/21 patients without/with needles. Visualization and quantitative results show the effectiveness of our proposed workflow. Specifically, our approach can correctly detect 95% needles with a tip location error of 1.01 mm on the prostate dataset. This technique could provide accurate needle detection for US-guided high-dose-rate prostate brachytherapy and facilitate the clinical workflow.