Guided ultrasound imaging using a deep regression network

Jenish Marharjan; Benjamin R. Mitchell; Vincent W. S. Chan; Edward Kim

doi:10.1117/12.2549428

16 March 2020 Guided ultrasound imaging using a deep regression network

Jenish Marharjan, Benjamin R. Mitchell, Vincent W. S. Chan, Edward Kim

Proceedings Volume 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography; 1131907 (2020) https://doi.org/10.1117/12.2549428
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Jenish Marharjan, Benjamin R. Mitchell, Vincent W. S. Chan, and Edward Kim "Guided ultrasound imaging using a deep regression network", Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 1131907 (16 March 2020); https://doi.org/10.1117/12.2549428

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