Identifying interventional device in medical images

Abstract

Images may be preprocessed to select pixels or voxels of interest prior to being analyzed by a neural network. Only the pixels or voxels of interest may be analyzed by the neural network to identify an object of interest. One or more slices may be extracted from the voxels of interest and provided to the neural network for analysis. The object may be further localized after identification by the neural network. The preprocessing, analysis by the neural network, and / or localization may utilize pre-existing knowledge of the object to be identified.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to US Provisional Application US 62 / 754250, filed November 1, 2018, and US Provisional Application US 62 / 909392, filed October 2, 2019, the contents of which are hereby incorporated by reference for any purpose Incorporated into this article. technical field [0003] The present disclosure relates to imaging systems and methods for identifying objects in images. In particular, imaging systems and methods for identifying interventional devices in medical images. Background technique [0004] Clinicians rely on medical images before, during and after interventional procedures. Medical images provide insight into the underlying tissue beneath the surface of the skin and also allow clinicians to see foreign objects within the body. During interventional procedures, medical images can be particularly useful in allowing a clinician to see the location of medical devices (eg, catheters, gui...

AI Technical Summary

Problems solved by technology

Despite these advantages, images generated from ultrasound often have low resolution and low contrast in 3D space, making it difficult for clinicians to position medical equipment in time during procedures

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