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US 12,755,384 B2Granted
Method for analyzing digital images
AssigneeHoffmann-La Roche Inc.
InventorsFilippo Arcadu, Maria Cristina De Vera Mudry, Citlalli Gámez Serna, Marco Tecilla
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Abstract
Systems and methods disclosed herein relate generally to systems and methods for detection, segmentation and characterization of isolated or overlapping object instances in digital images, applicable for detection, segmentation and characterization of crypts in histological images from patients with gastrointestinal disorders.
Claims17
A system comprising:
- a) an input/output unit configured to receive an input image that comprises at least one object of interest;
- and
- b) a processor, configured to perform sequentially the steps of:
- (i) obtaining using a first deep neural network from the input image at least a first Region of Interest (RoI) that includes the said at least one object of interest, and
- (ii) classifying using a second deep neural network, executed on the first RoI, the at least one object of interest in the first RoI by returning a class for the at least one object of interest, and
- (iii) detecting using a third deep neural network, executed on the at least one object of interest classified with the class, the at least one object of interest within the first RoI by returning a bounding box around the at least one object of interest classified with the class, and
- (iv) segmenting, using a binary mask generated by a fourth deep neural network, executed on the at least one object of interest within the bounding box, the at least one object of interest classified with the class and detected within the first RoI, and
- (v) extracting features from the at least one object of interest classified with the class, detected within the first RoI and segmented using the binary mask, wherein the features comprise shape, size and/or distribution of the objects.
5 more elements
A computer-implemented method comprising:
- (i) receiving an input image from a database comprising at least one object of interest, and
- (ii) obtaining using a first deep neural network from the input image at least a first Region of Interest that includes the said at least one object of interest, and
- (iii) classifying using a second deep neural network executed on the first RoI, the at least one object of interest in the first RoI by returning a class for the at least one object of interest, and
- (iv) detecting using a third deep neural network executed on the at least one object of interest classified with the class, the at least one object of interest within the first RoI, by returning a bounding box around the at least one object of interest classified with the class, and
- (v) segmenting, using a binary mask generated by a fourth deep neural network, executed on the at least one object of interest within the bounding box, the at least one classified and detected object of interest, and
- (vi) extracting features from the said at least one classified, detected and segmented object of interest using the binary mask, wherein the features comprise shape, size and/or distribution of the objects.
3 more elements
Dependencies are inferred from the claim text.