The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 19, 2021

Filed:

Apr. 26, 2018
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Ahmed El Harouni, San Jose, CA (US);

Mehdi Moradi, San Jose, CA (US);

Prasanth Prasanna, San Jose, CA (US);

Tanveer F. Syeda-Mahmood, Cupertino, CA (US);

Hui Tang, San Jose, CA (US);

Gopalkrishna Veni, San Jose, CA (US);

Hongzhi Wang, Santa Clara, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); G06N 3/08 (2006.01); G16H 30/20 (2018.01); G06T 7/00 (2017.01); G06T 7/194 (2017.01); G06K 9/66 (2006.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G06K 9/66 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06T 7/194 (2017.01); G16H 30/20 (2018.01); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30101 (2013.01);
Abstract

A method comprises (a) collecting (i) a set of chest computed tomography angiography (CTA) images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple organs; (b) preprocessing the images such that they share the same field of view (FOV); (c) using both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is the summation of the dice scores for all the multiple organs, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the organs; (d) testing a given (input) pre-processed image on the trained network, thereby obtaining segmented output of the given image; and (e) smoothing the segmented output of the given image.


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