Conference proceeding
Guided Ultrasound Imaging using a Deep Regression Network
MEDICAL IMAGING 2020: ULTRASONIC IMAGING AND TOMOGRAPHY, v 11319, pp 1131907-1131907-9
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this work, we present a machine learning method to guide an ultrasound operator towards a selected area of interest. Unlike other automatic medical imaging methods, ultrasound imaging is one of the few imaging modalities where the operator's skill and training are critical in obtaining high quality images. Additionally, due to recent advances in affordability and portability of ultrasound technology, its utilization by non-experts has increased. Thus, there is a growing need for intelligent systems that have the ability to assist ultrasound operators in both clinical and non-clinical scenarios. We propose a system that leverages machine learning to map real time ultrasound scans to transformation vectors that can guide a user to a target organ or anatomical structure. We present a unique training system that passively collects supervised training data from an expert sonographer and uses this data to train a deep regression network. Our results show that we are able to recognize anatomical structure through the use of ultrasound imaging and give the user guidance toward obtaining an ideal image.
Metrics
Details
- Title
- Guided Ultrasound Imaging using a Deep Regression Network
- Creators
- Jenish Maharjan - Villanova Univ, Dept Comp Sci, Villanova, PA 19085 USABenjamin R. Mitchell - Villanova UniversityVincent W. S. Chan - University of TorontoEdward Kim - Drexel University
- Contributors
- B C Byram (Editor)N V Ruiter (Editor)
- Publication Details
- MEDICAL IMAGING 2020: ULTRASONIC IMAGING AND TOMOGRAPHY, v 11319, pp 1131907-1131907-9
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000558363700004
- Scopus ID
- 2-s2.0-85082657525
- Other Identifier
- 991019168709604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Biomedical
- Optics
- Radiology, Nuclear Medicine & Medical Imaging