Method and system for machine learning based assessment of fractional flow reserve

Abstract

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Description

[0001] This application claims the benefit of U.S. Provisional Application No. 62 / 148,273, filed April 16, 2015, and U.S. Provisional Application No. 62 / 121,782, filed February 27, 2015, and filed October 16, 2014 Continuation-in-Part of filed U.S. Application No. 14 / 516,163 claiming the benefit of U.S. Provisional Application No. 61 / 891,920 filed October 17, 2013, the disclosures of which are incorporated by reference This article. technical field [0002] The present invention relates to anatomical object detection in medical image data, and more particularly, to anatomical object detection in medical image data using deep neural networks. Background technique [0003] Fast and robust anatomical object detection is a fundamental task in medical image analysis that underpins the entire clinical imaging workflow from diagnosis, patient stratification, treatment planning, intervention, and follow-up. Automatic detection of anatomical objects is a prerequisite for many medica...

AI Technical Summary

Problems solved by technology

However, anatomical object detection using MSL is not always robust, especially for some challenging detection

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