Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
TechRxiv
Abstract
Accurate needle placement is crucial during minimally
invasive procedures such as biopsies, regional anesthesia,
and fluid aspiration. 2D Ultrasound is widely used for needle
guidance during such procedures, however, it has a limited fieldof-
view and poor needle visibility for steep insertion angles.
In this work, we propose a novel machine learning (ML)-
based method for real-time needle segmentation in handheld
2D ultrasound systems. The proposed method involves a fast
and simple annotation technique allowing for the labeling of
large datasets. It then utilizes the U-Net architecture which is
modified to allow for easy integration into a handheld ultrasound
system. Two datasets were used in this work, one consisting
of B-mode ultrasound videos obtained from human tissue and
the other consisting of videos and frames from chicken, porcine
and bovine tissue. The model is trained on 1262 frames and
evaluated on 209 frames. This approach achieves an Intersection
Over Union (IoU) of 0.75 and a dice coefficient of 0.851 on
frames obtained from human tissue. The model is integrated
into the processing pipeline of a portable ultrasound system and
achieves an overall processing speed of about 8 frames per second.
The proposed approach outperforms state-of-the-art methods
for needle segmentation while achieving real-time integration.
This work is a step forward towards real-time needle guidance
using machine learning-based algorithms in handheld ultrasound
systems.
Description
Keywords
Ultrasound, Minimally invasive procedures, segmentation, U-Net, Real-time, Integration
Citation
Mugume Okwija, Paul; Nabacwa, Joanitta; Imanirakiza, Sylvia; Kimbowa, Alvin Bagetuuma; Mwikirize, Cosmas; Hacihaliloglu, Ilker; et al. (2022): Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.21234107.v1