CASPER: computer-aided segmentation of imperceptible motion—a learning-based tracking of an invisible needle in ultrasound

Abstract

Purpose

This paper presents a new micro-motion-based approach to track a needle in ultrasound images captured by a handheld transducer.

Methods

We propose a novel learning-based framework to track a handheld needle by detecting microscale variations of motion dynamics over time. The current state of the art on using motion analysis for needle detection uses absolute motion and hence work well only when the transducer is static. We have introduced and evaluated novel spatiotemporal and spectral features, obtained from the phase image, in a self-supervised tracking framework to improve the detection accuracy in the subsequent frames using incremental training. Our proposed tracking method involves volumetric feature selection and differential flow analysis to incorporate the neighboring pixels and mitigate the effects of the subtle tremor motion of a handheld transducer. To evaluate the detection accuracy, the method is tested on porcine tissue in-vivo, during the needle insertion in the biceps femoris muscle.

Results

Experimental results show the mean, standard deviation and root-mean-square errors of $1.28^{\circ }$, $1.09^{\circ }$ and $1.68^{\circ }$ in the insertion angle, and 0.82, 1.21, 1.47 mm, in the needle tip, respectively.

Conclusions

Compared to the appearance-based detection approaches, the proposed method is especially suitable for needles with ultrasonic characteristics that are imperceptible in the static image and to the naked eye.