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:
Apr. 06, 2021

Filed:

Mar. 18, 2019
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Youngjin Yoo, Princeton, NJ (US);

Mariappan S. Nadar, Plainsboro, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); A61B 5/055 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06K 9/6293 (2013.01); G06T 7/11 (2017.01); A61B 5/055 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20128 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Methods and systems are provided for automatically estimating image-level uncertainty for MS lesion segmentation data. A segmentation network is trained to segment MS lesions. The trained segmentation network is then used to estimate voxel level measures of uncertainty by performing Monte-Carlo (MC) dropout. The estimated voxel level uncertainty measures are converted into lesion level uncertainty measures. The information density of the lesion mask, the voxel level measures, and the lesion level measures is increased. A trained network receives input images, the segmented lesion masks, the voxel level uncertainty measures, and the lesion level uncertainty measures and outputs an image level uncertainty measure. The network is trained with a segmentation performance metric to predict an image level uncertainty measure on the segmented lesion mask that is produced by the trained segmentation network.


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