US 12,272,059 B2
System and method associated with predicting segmentation quality of objects in analysis of copious image data
Joel Haskin Saltz, Manhasset, NY (US); Tahsin M. Kurc, Coram, NY (US); Yi Gao, Stony Brook, NY (US); Wei Zhu, Setauket, NY (US); Si Wen, East Setauket, NY (US); Tianhao Zhao, Coram, NY (US); and Sampurna Shrestha, Stony Brook, NY (US)
Assigned to The Research Foundation for The State University of New York, Albany, NY (US)
Filed by The Research Foundation for The State University of New York, Albany, NY (US)
Filed on Jul. 26, 2023, as Appl. No. 18/226,482.
Application 18/226,482 is a continuation of application No. 16/605,404, granted, now 11,748,877, previously published as PCT/US2018/032026, filed on May 10, 2018.
Claims priority of provisional application 62/504,819, filed on May 11, 2017.
Prior Publication US 2024/0177301 A1, May 30, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/41 (2017.01); G06V 10/774 (2022.01); G06V 20/69 (2022.01)
CPC G06T 7/0012 (2013.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/2431 (2023.01); G06T 7/0002 (2013.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/41 (2017.01); G06V 10/774 (2022.01); G06V 20/695 (2022.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30168 (2013.01); G06T 2207/30181 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system to predict segmentation quality of segmented objects in image data, the system comprising:
a computing device; and
a non-transitory memory storing instructions that, when executed by the computing device, cause the computing device to perform operation comprising:
accessing a trained classifier comprising at least one classification model trained during a training phase, the at least one classification model trained based on a training set of images with segmented objects, the images being partitioned into regions of interest (ROIs), each region of the ROIs being further partitioned into patches, and intensity and texture features being computed at a patch-level for each of the patches;
receiving a test image with segmented objects during a classification phase in order to assess quality of segmentation of the segmented objects in the test image;
partitioning the test image into a plurality of patches of a size equivalent to patches of the training set of images;
computing intensity and texture features of the plurality of patches; and
applying the trained classifier using the at least one classification model to iteratively classify segmentation quality of each patch of the plurality of patches based on the intensity and texture features of the plurality of patches as computed during classification and the patch-level intensity and texture features as computed during the training phase, the segmentation quality of each patch as classified predicting a segmentation quality of the segmented objects in each patch of the plurality of patches in the test image.