Localization of partially visible needles in 3D ultrasound using dilated CNNs

Guidance of needles for interventions that involve percutaneous advancing of a needle to a target inside the patient's body is one of the key uses of ultrasound, such as for biopsies, ablations, and nerve blocks. During these procedures, image-based detection of the needle can circumvent complex needle-transducer alignment by ensuring an adequate visualization of the needle during the entire procedure. However, successful localization in the sector and curvilinear transducers is challenging as the needle can be invisible or partially visible, due to the lack of received beam reflections from parts of the needle. Therefore, it is necessary to explicitly model the global information present in the data for correct localization of the needle to compensate for the lost signal. We present a novel image-based localization technique to detect partially visible needles in phased-array 3D ultrasound volumes using dilated convolutional neural networks. The proposed algorithm successfully detects the needle plane with accuracy in the submillimeter domain in the 20 measured datasets, which also consists of the cases with mostly invisible needle shaft.