MACHINE LEARNING DETECTS A BIOPSY NEEDLE IN ULTRASOUND IMAGES

Localization of a biopsy needle in ultrasound (US) images is an important medical image analysis task, as it may help clinicians reduce the risk of damaging the tissue surrounding the cancer and spreading cancerous cells. Despite numerous studies dedicated to segmenting the needle from US, virtually all of them build upon the strong assumption that the needle is present in the image, which does not hold in clinical settings. We address this research gap and propose an end-to-end machine learning approach for biopsy needle detection in US images. The rigorous experimental study revealed that our approach delivers high-quality and fast operation, while offering a high level of flexibility---not only does it allow to update all blocks of the pipeline, but also to build a detection cascade for multi-scale analysis which dramatically reduces the number of sub-images undergoing classification, hence speeds up the detection process.