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.
Patent No.:
Date of Patent:
Sep. 05, 2023
Filed:
May. 10, 2018
The Research Foundation for the State University of New York, Albany, NY (US);
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);
Sampurna Shrestha, Stony Brook, NY (US);
The Research Foundation for The State University of New York, Albany, NY (US);
Abstract
A system associated with predicting segmentation quality of segmented objects implemented in the analysis of copious image data is disclosed. The system receives a collection of image data related to a particular type of data. The image data is segmented into segmented data portions based on an object associated with the collection of image data. Regions of interest associated with the segmented data portions are determined. The quality of segmentation of the segmented data portions is determined for respective classification of the regions of interest. A classification label is assigned to the regions of interest. Regions of interest are partitioned into sub-regions. Features associated with the sub-regions of the segmented data portions are determined. A training dataset is generated based on the determined features associated with the sub-regions in order to train a classification model based on a predetermined threshold value. Test images are received to iteratively classify segmented data portions based on an object associated with the test images, using the trained classification model. The segmentation quality of segmented objects in the test images is predicted based on the trained classification model. A corresponding method and computer-readable device are also disclosed.